Note: Descriptions are shown in the official language in which they were submitted.
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TECHNIQUES FOR MEASURING HEART RATE DURING EXERCISE
CROSS REFERENCE
[0001] The present Application for Patent claims the benefit of U.S. Non-
Provisional
Patent Application 17/954,564 by ZHANG et al., entitled "TECHNIQUES FOR
MEASURING HEART RATE DURING EXERCISE," filed September 28, 2022, which
claims the benefit of U.S. Provisional Patent Application No. 63/254,849 by
ZHANG et al.,
entitled "TECHNIQUES FOR MEASURING HEART RATE DURING EXERCISE," filed
October 12, 2021, assigned to the assignee hereof, and expressly incorporated
by reference
herein.
FIELD OF TECHNOLOGY
[0002] The following relates to wearable devices and data processing,
including
techniques for measuring heart rate during exercise.
BACKGROUND
[0003] Some wearable devices may be configured to collect data from users
associated
with heart rate of the user, such as motion data, temperature data,
photoplethysmogram (PPG)
data, etc. In some cases, some wearable devices may be unable to accurately
determine heart
rate data of a user, such as while the user is exercising or otherwise moving.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 illustrates an example of a system that supports techniques
for measuring
heart rate in accordance with aspects of the present disclosure.
[0005] FIG. 2 illustrates an example of a system that supports techniques
for measuring
heart rate in accordance with aspects of the present disclosure.
[0006] FIG. 3 illustrates an example of a heart rate determination
procedure that supports
techniques for measuring heart rate in accordance with aspects of the present
disclosure.
[0007] FIG. 4 illustrates an example of a heart rate determination
procedure that supports
techniques for measuring heart rate in accordance with aspects of the present
disclosure.
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[0008] FIG. 5 illustrates an example of a graphical user interface (GUI)
that supports
techniques for measuring heart rate in accordance with aspects of the present
disclosure.
[0009] FIG. 6 shows a block diagram of an apparatus that supports
techniques for
measuring heart rate in accordance with aspects of the present disclosure.
[0010] FIG. 7 shows a block diagram of a wearable application that supports
techniques
for measuring heart rate in accordance with aspects of the present disclosure.
[0011] FIG. 8 shows a diagram of a system including a device that supports
techniques
for measuring heart rate in accordance with aspects of the present disclosure.
[0012] FIGs. 9 through 11 show flowcharts illustrating methods that support
techniques
for measuring heart rate in accordance with aspects of the present disclosure.
DETAILED DESCRIPTION
[0013] A user may use a device (e.g., a wearable device) to determine
physiological
measurements of the user, such as heart rate. Some wearable devices may use
photoplethysmogram (PPG) data to determine a heart rate for a user over time.
For example,
a wearable device may measure heart rate of a user by detecting changes in
blood pulse
volume through PPG sensors in the wearable device (e.g., infrared PPG sensors,
infrared light
emitting diodes (LEDs)). Each time that a user's heart beats, blood is pumped
out to the
arteries located in the user's hands and fingers. The PPG sensors are able to
detect these
changes in blood flow and volume using light reflection and absorption. Each
pulse causes
the arteries in a user's finger to alternate between swelling and contracting.
By shining a light
on the skin of the user, particularly on the skin of a finger, changes in
light absorbed by the
blood and reflected back from the wavering volume of red blood cells in the
arteries is
measured. From here, PPG can represent these blood flow changes through a
visual
waveform that represents the activity of the user's heart (e.g., heart rate).
[0014] Heart rate is a sensitive metric that is subject to change based on
activities being
performed by a user (e.g., drinking a glass of water, standing up, watching
television,
exercise). Certain activities may result in a spike or dip in heart rate, and
such variations may
be referred to as "noise" in the data. Additionally, some activities may
produce a PPG signal
that may be misinterpreted as a heart rate signal. For example, exercises such
as running,
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jumping, etc. may cause motion artifacts (e.g., a false PPG signal). The
optical nature of PPG
sensors may render the PPG measurements susceptible to motion artifacts from
variable and
discontinuous contact between the device and skin. Motion artifacts are
typically caused by
the change of blood flow velocity induced by the motion of the exercise or the
relative
movement between PPG sensors and the skin of the user. For example, some
motions (e.g.,
running) may result in a periodic pressure between the wearable device (e.g.,
the sensors of
the wearable device) and the part of the user that the wearable device is
positioned, and as
such may apply periodic pressure to blood vessels. The periodic pressure
caused by the
periodic motion may cause the blood vessels to periodically constrict and
dilate. Accordingly,
the wearable device may detect such constriction and dilation of the vessels
as an artificial
PPG signal because the constriction and dilation were due to the motion of the
user, and not
due to the heart rate of the user. Such artificial PPG signals that are
attributable to motion
may be referred to as "motion artifacts."
[0015] As such, due to these difficulties with measuring heart rate during
exercise or
other motion, some conventional wearable devices may not track a user's heart
rate
throughout the day and/or during periods of motion accurately, or at all. In
such cases, the
wearable device may provide only a limited depiction of the user's heart rate,
and therefore a
limited depiction of the user's overall health. Additionally, heart rate may
be a valuable tool
to a user during an exercise because heart rate may provide an indication of
effort and
intensity during the exercise. For example, a user may utilize heart rate
during an exercise to
determine whether to increase or decrease intensity of a workout. Accordingly,
it may be
beneficial to implement techniques to accurately determine heart rate of a
user during periods
of activity, such as during an exercise. The wearable devices described herein
may be
configured with a procedure for detecting heart data and distinguishing the
actual heart rate
data of the user from motion artifacts impacting the data.
[0016] Accordingly, aspects of the present disclosure are directed to
techniques for
measuring a heart rate of a user in such a manner that is less susceptible to
motion and other
noise. A procedure for determining heart rate may include receiving
physiological data
associated with the user, wherein the physiological data may include PPG data
and motion
data (e.g., acceleration data) collected throughout a first time interval via
a wearable device
associated with the user. In some cases, the motion data may refer to
acceleration data and
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may be used to determine periods of motion of the user. The method may include
determining a set of candidate heart rate measurements within the first time
interval based at
least in part on the PPG data. The set of candidate heart rate measurements
may include
artificial heart rate measurements (e.g., heart rate measurements that may be
attributable to
periodic motion or other activity). Accordingly, the method may include
selecting a first heart
rate measurement from the set of candidate heart rate measurements based on
the received
motion data. For example, the set of candidate heart rate measurements may be
compared to
the motion data to determine the candidate heart rate measurements that are
correlated with
motion. Thus, the heart rate data that is not due to motion may be selected.
The method may
be used to determine a first heart rate for the user within the first time
interval based on the
selected first heart rate measurement.
[0017] In some implementations, machine learning models or algorithms
(e.g., heuristic-
based models, deep learning models, regression-based models) may be used to
determine
heart rate measurements for the user. For example, a wearable device may
acquire PPG data
and motion data associated with a user. The acquired PPG and motion data may
be input into
a machine learning model that is configured to output a determined or
estimated heart rate for
the user. For instance, the machine learning model may be configured to
differentiate
between candidate heart rate measurements that are attributable to motion
artifacts from
candidate heart rate measurements that are indicative of the user's actual
heart rate. In such
cases, the machine learning model may be configured to select (e.g., identify,
estimate) a
candidate heart rate measurement, and determine a heart rate for the user
based on the
selected/estimated candidate heart rate measurements. By way of another
example, the
machine learning model may be configured to identify time-domain and/or
frequency-domain
features within the received PPG data and motion data (which may be used to
identify
candidate heart rate measurements), and may be configured to determine or
estimate a heart
rate for the user based on the identified features.
[0018] The procedures described herein may not be limited to determining
heart rate
during exercise. In some cases, the procedures described herein may be
utilized to detect
heart rate at all times, or regardless of activity level such that the
wearable device may utilize
the described techniques during periods of rest, activity, exercise, sleep,
etc. Accordingly,
particular aspects of the subject matter described herein may be implemented
to realize one or
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more advantages. The described techniques may support improvements in
detecting and
determining heart rate data of a user to provide the user with comprehensive
heart rate data
during periods of rest, activity, exercise, sleep, etc.
[0019] Aspects of the disclosure are initially described in the context of
systems
supporting physiological data collection from users via wearable devices.
Additional aspects
of the disclosure are additionally described in the context of example heart
rate determination
procedures and an example graphical user interface (GUI). Aspects of the
disclosure are
further illustrated by and described with reference to apparatus diagrams,
system diagrams,
and flowcharts that relate to techniques for measuring heart rate during
exercise.
[0020] FIG. 1 illustrates an example of a system 100 that supports
techniques for
measuring heart rate in accordance with aspects of the present disclosure. The
system 100
includes a plurality of electronic devices (e.g., wearable devices 104, user
devices 106) that
may be worn and/or operated by one or more users 102. The system 100 further
includes a
network 108 and one or more servers 110.
[0021] The electronic devices may include any electronic devices known in
the art,
including wearable devices 104 (e.g., ring wearable devices, watch wearable
devices, etc.),
user devices 106 (e.g., smartphones, laptops, tablets). The electronic devices
associated with
the respective users 102 may include one or more of the following
functionalities: 1)
measuring physiological data, 2) storing the measured data, 3) processing the
data, 4)
providing outputs (e.g., via GUIs) to a user 102 based on the processed data,
and 5)
communicating data with one another and/or other computing devices. Different
electronic
devices may perform one or more of the functionalities.
[0022] Example wearable devices 104 may include wearable computing devices,
such as
a ring computing device (hereinafter "ring") configured to be worn on a user's
102 finger, a
wrist computing device (e.g., a smart watch, fitness band, or bracelet)
configured to be worn
on a user's 102 wrist, and/or a head mounted computing device (e.g.,
glasses/goggles).
Wearable devices 104 may also include bands, straps (e.g., flexible or
inflexible bands or
straps), stick-on sensors, and the like, which may be positioned in other
locations, such as
bands around the head (e.g., a forehead headband), arm (e.g., a forearm band
and/or bicep
band), and/or leg (e.g., a thigh or calf band), behind the ear, under the
armpit, and the like.
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Wearable devices 104 may also be attached to, or included in, articles of
clothing. For
example, wearable devices 104 may be included in pockets and/or pouches on
clothing. As
another example, wearable device 104 may be clipped and/or pinned to clothing,
or may
otherwise be maintained within the vicinity of the user 102. Example articles
of clothing may
include, but are not limited to, hats, shirts, gloves, pants, socks, outerwear
(e.g., jackets), and
undergarments. In some implementations, wearable devices 104 may be included
with other
types of devices such as training/sporting devices that are used during
physical activity. For
example, wearable devices 104 may be attached to, or included in, a bicycle,
skis, a tennis
racket, a golf club, and/or training weights.
[0023] Much of the present disclosure may be described in the context of a
ring wearable
device 104. Accordingly, the terms "ring 104," "wearable device 104," and like
terms, may
be used interchangeably, unless noted otherwise herein. However, the use of
the term "ring
104" is not to be regarded as limiting, as it is contemplated herein that
aspects of the present
disclosure may be performed using other wearable devices (e.g., watch wearable
devices,
necklace wearable device, bracelet wearable devices, earring wearable devices,
anklet
wearable devices, and the like).
[0024] In some aspects, user devices 106 may include handheld mobile
computing
devices, such as smartphones and tablet computing devices. User devices 106
may also
include personal computers, such as laptop and desktop computing devices.
Other example
user devices 106 may include server computing devices that may communicate
with other
electronic devices (e.g., via the Internet). In some implementations,
computing devices may
include medical devices, such as external wearable computing devices (e.g.,
Holter monitors).
Medical devices may also include implantable medical devices, such as
pacemakers and
cardioverter defibrillators. Other example user devices 106 may include home
computing
devices, such as internet of things (IoT) devices (e.g., IoT devices), smart
televisions, smart
speakers, smart displays (e.g., video call displays), hubs (e.g., wireless
communication hubs),
security systems, smart appliances (e.g., thermostats and refrigerators), and
fitness
equipment.
[0025] Some electronic devices (e.g., wearable devices 104, user devices
106) may
measure physiological parameters of respective users 102, such as
photoplethysmography
waveforms, continuous skin temperature, a pulse waveform, respiration rate,
heart rate, heart
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rate variability (HRV), actigraphy, galvanic skin response, pulse oximetry,
and/or other
physiological parameters. Some electronic devices that measure physiological
parameters
may also perform some/all of the calculations described herein. Some
electronic devices may
not measure physiological parameters, but may perform some/all of the
calculations
described herein. For example, a ring (e.g., wearable device 104), mobile
device application,
or a server computing device may process received physiological data that was
measured by
other devices.
[0026] In some implementations, a user 102 may operate, or may be
associated with,
multiple electronic devices, some of which may measure physiological
parameters and some
of which may process the measured physiological parameters. In some
implementations, a
user 102 may have a ring (e.g., wearable device 104) that measures
physiological parameters.
The user 102 may also have, or be associated with, a user device 106 (e.g.,
mobile device,
smartphone), where the wearable device 104 and the user device 106 are
communicatively
coupled to one another. In some cases, the user device 106 may receive data
from the
wearable device 104 and perform some/all of the calculations described herein.
In some
implementations, the user device 106 may also measure physiological parameters
described
herein, such as motion/activity parameters.
[0027] For example, as illustrated in FIG. 1, a first user 102-a (User 1)
may operate, or
may be associated with, a wearable device 104-a (e.g., ring 104-a) and a user
device 106-a
that may operate as described herein. In this example, the user device 106-a
associated with
user 102-a may process/store physiological parameters measured by the ring 104-
a.
Comparatively, a second user 102-b (User 2) may be associated with a ring 104-
b, a watch
wearable device 104-c (e.g., watch 104-c), and a user device 106-b, where the
user device
106-b associated with user 102-b may process/store physiological parameters
measured by
the ring 104-b and/or the watch 104-c. Moreover, an nth user 102-n (User N)
may be
associated with an arrangement of electronic devices described herein (e.g.,
ring 104-n, user
device 106-n). In some aspects, wearable devices 104 (e.g., rings 104, watches
104) and other
electronic devices may be communicatively coupled to the user devices 106 of
the respective
users 102 via Bluetooth, Wi-Fi, and other wireless protocols.
[0028] In some implementations, the rings 104 (e.g., wearable devices 104)
of the system
100 may be configured to collect physiological data from the respective users
102 based on
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arterial blood flow within the user's finger. In particular, a ring 104 may
utilize one or more
LEDs (e.g., red LEDs, green LEDs) that emit light on the palm-side of a user's
finger to
collect physiological data based on arterial blood flow within the user's
finger. In some
implementations, the ring 104 may acquire the physiological data using a
combination of
both green and red LEDs. The physiological data may include any physiological
data known
in the art including, but not limited to, temperature data, accelerometer data
(e.g.,
movement/motion data), heart rate data, HRV data, blood oxygen level data, or
any
combination thereof
[0029] The use of both green and red LEDs may provide several advantages
over other
solutions, as red and green LEDs have been found to have their own distinct
advantages when
acquiring physiological data under different conditions (e.g., light/dark,
active/inactive) and
via different parts of the body, and the like. For example, green LEDs have
been found to
exhibit better performance during exercise. Moreover, using multiple LEDs
(e.g., green and
red LEDs) distributed around the ring 104 has been found to exhibit superior
performance as
compared to wearable devices that utilize LEDs which are positioned close to
one another,
such as within a watch wearable device. Furthermore, the blood vessels in the
finger (e.g.,
arteries, capillaries) are more accessible via LEDs as compared to blood
vessels in the wrist.
In particular, arteries in the wrist are positioned on the bottom of the wrist
(e.g., palm-side of
the wrist), meaning only capillaries are accessible on the top of the wrist
(e.g., back of hand
side of the wrist), where wearable watch devices and similar devices are
typically worn. As
such, utilizing LEDs and other sensors within a ring 104 has been found to
exhibit superior
performance as compared to wearable devices worn on the wrist, as the ring 104
may have
greater access to arteries (as compared to capillaries), thereby resulting in
stronger signals
and more valuable physiological data.
[0030] The electronic devices of the system 100 (e.g., user devices 106,
wearable devices
104) may be communicatively coupled to one or more servers 110 via wired or
wireless
communication protocols. For example, as shown in FIG. 1, the electronic
devices (e.g., user
devices 106) may be communicatively coupled to one or more servers 110 via a
network 108.
The network 108 may implement transfer control protocol and internet protocol
(TCP/IP),
such as the Internet, or may implement other network 108 protocols. Network
connections
between the network 108 and the respective electronic devices may facilitate
transport of data
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via email, web, text messages, mail, or any other appropriate form of
interaction within a
computer network 108. For example, in some implementations, the ring 104-a
associated
with the first user 102-a may be communicatively coupled to the user device
106-a, where the
user device 106-a is communicatively coupled to the servers 110 via the
network 108. In
additional or alternative cases, wearable devices 104 (e.g., rings 104,
watches 104) may be
directly communicatively coupled to the network 108.
[0031] The system 100 may offer an on-demand database service between the
user
devices 106 and the one or more servers 110. In some cases, the servers 110
may receive data
from the user devices 106 via the network 108, and may store and analyze the
data. Similarly,
the servers 110 may provide data to the user devices 106 via the network 108.
In some cases,
the servers 110 may be located at one or more data centers. The servers 110
may be used for
data storage, management, and processing. In some implementations, the servers
110 may
provide a web-based interface to the user device 106 via web browsers.
[0032] In some aspects, the system 100 may detect periods of time that a
user 102 is
asleep, and classify periods of time that the user 102 is asleep into one or
more sleep stages
(e.g., sleep stage classification). For example, as shown in FIG. 1, User 102-
a may be
associated with a wearable device 104-a (e.g., ring 104-a) and a user device
106-a. In this
example, the ring 104-a may collect physiological data associated with the
user 102-a,
including temperature, heart rate, HRV, respiratory rate, and the like. In
some aspects, data
collected by the ring 104-a may be input to a machine learning classifier,
where the machine
learning classifier is configured to determine periods of time that the user
102-a is (or was)
asleep. Moreover, the machine learning classifier may be configured to
classify periods of
time into different sleep stages, including an awake sleep stage, a rapid eye
movement (REM)
sleep stage, a light sleep stage (non-REM (NREM)), and a deep sleep stage
(NREM). In some
aspects, the classified sleep stages may be displayed to the user 102-a via a
GUI of the user
device 106-a. Sleep stage classification may be used to provide feedback to a
user 102-a
regarding the user's sleeping patterns, such as recommended bedtimes,
recommended wake-
up times, and the like. Moreover, in some implementations, sleep stage
classification
techniques described herein may be used to calculate scores for the respective
user, such as
Sleep Scores, Readiness Scores, and the like.
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[0033] In some aspects, the system 100 may utilize circadian rhythm-derived
features to
further improve physiological data collection, data processing procedures, and
other
techniques described herein. The term circadian rhythm may refer to a natural,
internal
process that regulates an individual's sleep-wake cycle, which repeats
approximately every
24 hours. In this regard, techniques described herein may utilize circadian
rhythm adjustment
models to improve physiological data collection, analysis, and data
processing. For example,
a circadian rhythm adjustment model may be input into a machine learning
classifier along
with physiological data collected from the user 102-a via the wearable device
104-a. In this
example, the circadian rhythm adjustment model may be configured to "weight,"
or adjust,
physiological data collected throughout a user's natural, approximately 24-
hour circadian
rhythm. In some implementations, the system may initially start with a
"baseline" circadian
rhythm adjustment model, and may modify the baseline model using physiological
data
collected from each user 102 to generate tailored, individualized circadian
rhythm adjustment
models that are specific to each respective user 102.
[0034] In some aspects, the system 100 may utilize other biological rhythms
to further
improve physiological data collection, analysis, and processing by phase of
these other
rhythms. For example, if a weekly rhythm is detected within an individual's
baseline data,
then the model may be configured to adjust "weights" of data by day of the
week. Biological
rhythms that may require adjustment to the model by this method include: 1)
ultradian (faster
than a day rhythms, including sleep cycles in a sleep state, and oscillations
from less than an
hour to several hours periodicity in the measured physiological variables
during wake state;
2) circadian rhythms; 3) non-endogenous daily rhythms shown to be imposed on
top of
circadian rhythms, as in work schedules; 4) weekly rhythms, or other
artificial time
periodicities exogenously imposed (e.g., in a hypothetical culture with 12 day
"weeks", 12
day rhythms could be used); 5) multi-day ovarian rhythms in women and
spermatogenesis
rhythms in men; 6) lunar rhythms (relevant for individuals living with low or
no artificial
lights); and 7) seasonal rhythms.
[0035] The biological rhythms are not always stationary rhythms. For
example, many
women experience variability in ovarian cycle length across cycles, and
ultradian rhythms are
not expected to occur at exactly the same time or periodicity across days even
within a user.
As such, signal processing techniques sufficient to quantify the frequency
composition while
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preserving temporal resolution of these rhythms in physiological data may be
used to
improve detection of these rhythms, to assign phase of each rhythm to each
moment in time
measured, and to thereby modify adjustment models and comparisons of time
intervals. The
biological rhythm-adjustment models and parameters can be added in linear or
non-linear
combinations as appropriate to more accurately capture the dynamic
physiological baselines
of an individual or group of individuals.
[0036] In some aspects, the respective devices of the system 100 may
support techniques
for determining heart rate data of a user based on physiological data (e.g.,
motion data)
collected by a wearable device. The system may support techniques for
determining heart rate
data during periods of activity, motion, exercise, etc. In particular, the
system 100 illustrated
in FIG. 1 may support techniques for determining heart rate data of a user
102, and causing a
user device 106 corresponding to the user 102 to display an indication of the
heart rate data.
For example, as shown in FIG. 1, User 1 (user 102-a) may be associated with a
wearable
device 104-a (e.g., ring 104-a) and a user device 106-a. In this example, the
ring 104-a may
collect data associated with the user 102-a, including motion, temperature,
heart rate, HRV,
and the like. In some aspects, the ring 104-a may be used to collect
physiological data of the
user that the ring 104-a may use to select actual heart rate data from motion
artifacts. The ring
104-a may determine heart rate data based on the PPG monitoring.
[0037] Physiological data collection, PPG monitoring, and heart rate data
determination
may be performed by any of the components of the system 100, including the
ring 104-a, the
user device 106-a associated with User 1, the one or more servers 110, or any
combination
thereof For example, in some implementations, collected PPG data and motion
data may be
inputted into a machine learning model that is configured to determine a heart
rate for the
user. Upon determination of heart rate data, the system 100 may selectively
cause the GUI of
the user device 106-a to display all or a subset of the heart rate data.
[0038] It should be appreciated by a person skilled in the art that one or
more aspects of
the disclosure may be implemented in a system 100 to additionally or
alternatively solve
other problems than those described above. Furthermore, aspects of the
disclosure may
provide technical improvements to "conventional" systems or processes as
described herein.
However, the description and appended drawings only include example technical
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improvements resulting from implementing aspects of the disclosure, and
accordingly do not
represent all of the technical improvements provided within the scope of the
claims.
[0039] FIG. 2 illustrates an example of a system 200 that supports
techniques for
measuring heart rate in accordance with aspects of the present disclosure. The
system 200
may implement, or be implemented by, system 100. In particular, system 200
illustrates an
example of a ring 104 (e.g., wearable device 104), a user device 106, and a
server 110, as
described with reference to FIG. 1.
[0040] In some aspects, the ring 104 may be configured to be worn around a
user's
finger, and may determine one or more user physiological parameters when worn
around the
user's finger. Example measurements and determinations may include, but are
not limited to,
user skin temperature, pulse waveforms, respiratory rate, heart rate, HRV,
blood oxygen
levels, and the like.
[0041] The system 200 further includes a user device 106 (e.g., a
smartphone) in
communication with the ring 104. For example, the ring 104 may be in wireless
and/or wired
communication with the user device 106. In some implementations, the ring 104
may send
measured and processed data (e.g., temperature data, PPG data,
motion/accelerometer data,
ring input data, and the like) to the user device 106. The user device 106 may
also send data
to the ring 104, such as ring 104 firmware/configuration updates. The user
device 106 may
process data. In some implementations, the user device 106 may transmit data
to the server
110 for processing and/or storage.
[0042] The ring 104 may include a housing 205, which may include an inner
housing
205-a and an outer housing 205-b. In some aspects, the housing 205 of the ring
104 may store
or otherwise include various components of the ring including, but not limited
to, device
electronics, a power source (e.g., battery 210, and/or capacitor), one or more
substrates (e.g.,
printable circuit boards) that interconnect the device electronics and/or
power source, and the
like. The device electronics may include device modules (e.g.,
hardware/software), such as: a
processing module 230-a, a memory 215, a communication module 220-a, a power
module
225, and the like. The device electronics may also include one or more
sensors. Example
sensors may include one or more temperature sensors 240, a PPG sensor assembly
(e.g., PPG
system 235), and one or more motion sensors 245.
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[0043] The sensors may include associated modules (not illustrated)
configured to
communicate with the respective components/modules of the ring 104, and
generate signals
associated with the respective sensors. In some aspects, each of the
components/modules of
the ring 104 may be communicatively coupled to one another via wired or
wireless
connections. Moreover, the ring 104 may include additional and/or alternative
sensors or
other components that are configured to collect physiological data from the
user, including
light sensors (e.g., LEDs), oximeters, and the like.
[0044] The ring 104 shown and described with reference to FIG. 2 is
provided solely for
illustrative purposes. As such, the ring 104 may include additional or
alternative components
as those illustrated in FIG. 2. Other rings 104 that provide functionality
described herein may
be fabricated. For example, rings 104 with fewer components (e.g., sensors)
may be
fabricated. In a specific example, a ring 104 with a single temperature sensor
240 (or other
sensor), a power source, and device electronics configured to read the single
temperature
sensor 240 (or other sensor) may be fabricated. In another specific example, a
temperature
sensor 240 (or other sensor) may be attached to a user's finger (e.g., using a
clamps, spring
loaded clamps, etc.). In this case, the sensor may be wired to another
computing device, such
as a wrist worn computing device that reads the temperature sensor 240 (or
other sensor). In
other examples, a ring 104 that includes additional sensors and processing
functionality may
be fabricated.
[0045] The housing 205 may include one or more housing 205 components. The
housing
205 may include an outer housing 205-b component (e.g., a shell) and an inner
housing 205-a
component (e.g., a molding). The housing 205 may include additional components
(e.g.,
additional layers) not explicitly illustrated in FIG. 2. For example, in some
implementations,
the ring 104 may include one or more insulating layers that electrically
insulate the device
electronics and other conductive materials (e.g., electrical traces) from the
outer housing 205-
b (e.g., a metal outer housing 205-b). The housing 205 may provide structural
support for the
device electronics, battery 210, substrate(s), and other components. For
example, the housing
205 may protect the device electronics, battery 210, and substrate(s) from
mechanical forces,
such as pressure and impacts. The housing 205 may also protect the device
electronics,
battery 210, and substrate(s) from water and/or other chemicals.
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[0046] The outer housing 205-b may be fabricated from one or more
materials. In some
implementations, the outer housing 205-b may include a metal, such as
titanium, which may
provide strength and abrasion resistance at a relatively light weight. The
outer housing 205-b
may also be fabricated from other materials, such polymers. In some
implementations, the
outer housing 205-b may be protective as well as decorative.
[0047] The inner housing 205-a may be configured to interface with the
user's finger.
The inner housing 205-a may be formed from a polymer (e.g., a medical grade
polymer) or
other material. In some implementations, the inner housing 205-a may be
transparent. For
example, the inner housing 205-a may be transparent to light emitted by the
PPG light
emitting diodes (LEDs). In some implementations, the inner housing 205-a
component may
be molded onto the outer housing 205-b. For example, the inner housing 205-a
may include a
polymer that is molded (e.g., injection molded) to fit into an outer housing
205-b metallic
shell.
[0048] The ring 104 may include one or more substrates (not illustrated).
The device
electronics and battery 210 may be included on the one or more substrates. For
example, the
device electronics and battery 210 may be mounted on one or more substrates.
Example
substrates may include one or more printed circuit boards (PCBs), such as
flexible PCB (e.g.,
polyimide). In some implementations, the electronics/battery 210 may include
surface
mounted devices (e.g., surface-mount technology (SMT) devices) on a flexible
PCB. In some
implementations, the one or more substrates (e.g., one or more flexible PCBs)
may include
electrical traces that provide electrical communication between device
electronics. The
electrical traces may also connect the battery 210 to the device electronics.
[0049] The device electronics, battery 210, and substrates may be arranged
in the ring
104 in a variety of ways. In some implementations, one substrate that includes
device
electronics may be mounted along the bottom of the ring 104 (e.g., the bottom
half), such that
the sensors (e.g., PPG system 235, temperature sensors 240, motion sensors
245, and other
sensors) interface with the underside of the user's finger. In these
implementations, the
battery 210 may be included along the top portion of the ring 104 (e.g., on
another substrate).
[0050] The various components/modules of the ring 104 represent
functionality (e.g.,
circuits and other components) that may be included in the ring 104. Modules
may include
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any discrete and/or integrated electronic circuit components that implement
analog and/or
digital circuits capable of producing the functions attributed to the modules
herein. For
example, the modules may include analog circuits (e.g., amplification
circuits, filtering
circuits, analog/digital conversion circuits, and/or other signal conditioning
circuits). The
modules may also include digital circuits (e.g., combinational or sequential
logic circuits,
memory circuits etc.).
[0051] The memory 215 (memory module) of the ring 104 may include any
volatile, non-
volatile, magnetic, or electrical media, such as a random access memory (RAM),
read-only
memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM
(EEPROM), flash memory, or any other memory device. The memory 215 may store
any of
the data described herein. For example, the memory 215 may be configured to
store data
(e.g., motion data, temperature data, PPG data) collected by the respective
sensors and PPG
system 235. Furthermore, memory 215 may include instructions that, when
executed by one
or more processing circuits, cause the modules to perform various functions
attributed to the
modules herein. The device electronics of the ring 104 described herein are
only example
device electronics. As such, the types of electronic components used to
implement the device
electronics may vary based on design considerations.
[0052] The functions attributed to the modules of the ring 104 described
herein may be
embodied as one or more processors, hardware, firmware, software, or any
combination
thereof Depiction of different features as modules is intended to highlight
different
functional aspects and does not necessarily imply that such modules must be
realized by
separate hardware/software components. Rather, functionality associated with
one or more
modules may be performed by separate hardware/software components or
integrated within
common hardware/software components.
[0053] The processing module 230-a of the ring 104 may include one or more
processors
(e.g., processing units), microcontrollers, digital signal processors, systems
on a chip (SOCs),
and/or other processing devices. The processing module 230-a communicates with
the
modules included in the ring 104. For example, the processing module 230-a may
transmit/receive data to/from the modules and other components of the ring
104, such as the
sensors. As described herein, the modules may be implemented by various
circuit
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components. Accordingly, the modules may also be referred to as circuits
(e.g., a
communication circuit and power circuit).
[0054] The processing module 230-a may communicate with the memory 215. The
memory 215 may include computer-readable instructions that, when executed by
the
processing module 230-a, cause the processing module 230-a to perform the
various
functions attributed to the processing module 230-a herein. In some
implementations, the
processing module 230-a (e.g., a microcontroller) may include additional
features associated
with other modules, such as communication functionality provided by the
communication
module 220-a (e.g., an integrated Bluetooth Low Energy transceiver) and/or
additional
onboard memory 215.
[0055] The communication module 220-a may include circuits that provide
wireless
and/or wired communication with the user device 106 (e.g., communication
module 220-b of
the user device 106). In some implementations, the communication modules 220-
a, 220-b
may include wireless communication circuits, such as Bluetooth circuits and/or
Wi-Fi
circuits. In some implementations, the communication modules 220-a, 220-b can
include
wired communication circuits, such as Universal Serial Bus (USB) communication
circuits.
Using the communication module 220-a, the ring 104 and the user device 106 may
be
configured to communicate with each other. The processing module 230-a of the
ring may be
configured to transmit/receive data to/from the user device 106 via the
communication
module 220-a. Example data may include, but is not limited to, motion data,
temperature
data, pulse waveforms, heart rate data, HRV data, PPG data, and status updates
(e.g.,
charging status, battery charge level, and/or ring 104 configuration
settings). The processing
module 230-a of the ring may also be configured to receive updates (e.g.,
software/firmware
updates) and data from the user device 106.
[0056] The ring 104 may include a battery 210 (e.g., a rechargeable battery
210). An
example battery 210 may include a Lithium-Ion or Lithium-Polymer type battery
210,
although a variety of battery 210 options are possible. The battery 210 may be
wirelessly
charged. In some implementations, the ring 104 may include a power source
other than the
battery 210, such as a capacitor. The power source (e.g., battery 210 or
capacitor) may have a
curved geometry that matches the curve of the ring 104. In some aspects, a
charger or other
power source may include additional sensors that may be used to collect data
in addition to,
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or which supplements, data collected by the ring 104 itself Moreover, a
charger or other
power source for the ring 104 may function as a user device 106, in which case
the charger or
other power source for the ring 104 may be configured to receive data from the
ring 104,
store and/or process data received from the ring 104, and communicate data
between the ring
104 and the servers 110.
[0057] In some aspects, the ring 104 includes a power module 225 that may
control
charging of the battery 210. For example, the power module 225 may interface
with an
external wireless charger that charges the battery 210 when interfaced with
the ring 104. The
charger may include a datum structure that mates with a ring 104 datum
structure to create a
specified orientation with the ring 104 during 104 charging. The power module
225 may also
regulate voltage(s) of the device electronics, regulate power output to the
device electronics,
and monitor the state of charge of the battery 210. In some implementations,
the battery 210
may include a protection circuit module (PCM) that protects the battery 210
from high
current discharge, over voltage during 104 charging, and under voltage during
104 discharge.
The power module 225 may also include electro-static discharge (ESD)
protection.
[0058] The one or more temperature sensors 240 may be electrically coupled
to the
processing module 230-a. The temperature sensor 240 may be configured to
generate a
temperature signal (e.g., temperature data) that indicates a temperature read
or sensed by the
temperature sensor 240. The processing module 230-a may determine a
temperature of the
user in the location of the temperature sensor 240. For example, in the ring
104, temperature
data generated by the temperature sensor 240 may indicate a temperature of a
user at the
user's finger (e.g., skin temperature). In some implementations, the
temperature sensor 240
may contact the user's skin. In other implementations, a portion of the
housing 205 (e.g., the
inner housing 205-a) may form a barrier (e.g., a thin, thermally conductive
barrier) between
the temperature sensor 240 and the user's skin. In some implementations,
portions of the ring
104 configured to contact the user's finger may have thermally conductive
portions and
thermally insulative portions. The thermally conductive portions may conduct
heat from the
user's finger to the temperature sensors 240. The thermally insulative
portions may insulate
portions of the ring 104 (e.g., the temperature sensor 240) from ambient
temperature.
[0059] In some implementations, the temperature sensor 240 may generate a
digital
signal (e.g., temperature data) that the processing module 230-a may use to
determine the
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temperature. As another example, in cases where the temperature sensor 240
includes a
passive sensor, the processing module 230-a (or a temperature sensor 240
module) may
measure a current/voltage generated by the temperature sensor 240 and
determine the
temperature based on the measured current/voltage. Example temperature sensors
240 may
include a thermistor, such as a negative temperature coefficient (NTC)
thermistor, or other
types of sensors including resistors, transistors, diodes, and/or other
electrical/electronic
components.
[0060] The processing module 230-a may sample the user's temperature over
time. For
example, the processing module 230-a may sample the user's temperature
according to a
sampling rate. An example sampling rate may include one sample per second,
although the
processing module 230-a may be configured to sample the temperature signal at
other
sampling rates that are higher or lower than one sample per second. In some
implementations,
the processing module 230-a may sample the user's temperature continuously
throughout the
day and night. Sampling at a sufficient rate (e.g., one sample per second)
throughout the day
may provide sufficient temperature data for analysis described herein.
[0061] The processing module 230-a may store the sampled temperature data
in memory
215. In some implementations, the processing module 230-a may process the
sampled
temperature data. For example, the processing module 230-a may determine
average
temperature values over a period of time. In one example, the processing
module 230-a may
determine an average temperature value each minute by summing all temperature
values
collected over the minute and dividing by the number of samples over the
minute. In a
specific example where the temperature is sampled at one sample per second,
the average
temperature may be a sum of all sampled temperatures for one minute divided by
sixty
seconds. The memory 215 may store the average temperature values over time. In
some
implementations, the memory 215 may store average temperatures (e.g., one per
minute)
instead of sampled temperatures in order to conserve memory 215.
[0062] The sampling rate, which may be stored in memory 215, may be
configurable. In
some implementations, the sampling rate may be the same throughout the day and
night. In
other implementations, the sampling rate may be changed throughout the
day/night. In some
implementations, the ring 104 may filter/reject temperature readings, such as
large spikes in
temperature that are not indicative of physiological changes (e.g., a
temperature spike from a
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hot shower). In some implementations, the ring 104 may filter/reject
temperature readings
that may not be reliable due to other factors, such as excessive motion during
104 exercise
(e.g., as indicated by a motion sensor 245).
[0063] The ring 104 (e.g., communication module) may transmit the sampled
and/or
average temperature data to the user device 106 for storage and/or further
processing. The
user device 106 may transfer the sampled and/or average temperature data to
the server 110
for storage and/or further processing.
[0064] Although the ring 104 is illustrated as including a single
temperature sensor 240,
the ring 104 may include multiple temperature sensors 240 in one or more
locations, such as
arranged along the inner housing 205-a near the user's finger. In some
implementations, the
temperature sensors 240 may be stand-alone temperature sensors 240.
Additionally, or
alternatively, one or more temperature sensors 240 may be included with other
components
(e.g., packaged with other components), such as with the accelerometer and/or
processor.
[0065] The processing module 230-a may acquire and process data from
multiple
temperature sensors 240 in a similar manner described with respect to a single
temperature
sensor 240. For example, the processing module 230 may individually sample,
average, and
store temperature data from each of the multiple temperature sensors 240. In
other examples,
the processing module 230-a may sample the sensors at different rates and
average/store
different values for the different sensors. In some implementations, the
processing module
230-a may be configured to determine a single temperature based on the average
of two or
more temperatures determined by two or more temperature sensors 240 in
different locations
on the finger.
[0066] The temperature sensors 240 on the ring 104 may acquire distal
temperatures at
the user's finger (e.g., any finger). For example, one or more temperature
sensors 240 on the
ring 104 may acquire a user's temperature from the underside of a finger or at
a different
location on the finger. In some implementations, the ring 104 may continuously
acquire distal
temperature (e.g., at a sampling rate). Although distal temperature measured
by a ring 104 at
the finger is described herein, other devices may measure temperature at the
same/different
locations. In some cases, the distal temperature measured at a user's finger
may differ from
the temperature measured at a user's wrist or other external body location.
Additionally, the
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distal temperature measured at a user's finger (e.g., a "shell" temperature)
may differ from
the user's core temperature. As such, the ring 104 may provide a useful
temperature signal
that may not be acquired at other internal/external locations of the body. In
some cases,
continuous temperature measurement at the finger may capture temperature
fluctuations (e.g.,
small or large fluctuations) that may not be evident in core temperature. For
example,
continuous temperature measurement at the finger may capture minute-to-minute
or hour-to-
hour temperature fluctuations that provide additional insight that may not be
provided by
other temperature measurements elsewhere in the body.
[0067] The ring 104 may include a PPG system 235. The PPG system 235 may
include
one or more optical transmitters that transmit light. The PPG system 235 may
also include
one or more optical receivers that receive light transmitted by the one or
more optical
transmitters. An optical receiver may generate a signal (hereinafter "PPG"
signal) that
indicates an amount of light received by the optical receiver. The optical
transmitters may
illuminate a region of the user's finger. The PPG signal generated by the PPG
system 235
may indicate the perfusion of blood in the illuminated region. For example,
the PPG signal
may indicate blood volume changes in the illuminated region caused by a user's
pulse
pressure. The processing module 230-a may sample the PPG signal and determine
a user's
pulse waveform based on the PPG signal. The processing module 230-a may
determine a
variety of physiological parameters based on the user's pulse waveform, such
as a user's
respiratory rate, heart rate, HRV, oxygen saturation, and other circulatory
parameters.
[0068] In some implementations, the PPG system 235 may be configured as a
reflective
PPG system 235 in which the optical receiver(s) receive transmitted light that
is reflected
through the region of the user's finger. In some implementations, the PPG
system 235 may be
configured as a transmissive PPG system 235 in which the optical
transmitter(s) and optical
receiver(s) are arranged opposite to one another, such that light is
transmitted directly through
a portion of the user's finger to the optical receiver(s).
[0069] The number and ratio of transmitters and receivers included in the
PPG system
235 may vary. Example optical transmitters may include light-emitting diodes
(LEDs). The
optical transmitters may transmit light in the infrared spectrum and/or other
spectrums.
Example optical receivers may include, but are not limited to, photosensors,
phototransistors,
and photodiodes. The optical receivers may be configured to generate PPG
signals in
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response to the wavelengths received from the optical transmitters. The
location of the
transmitters and receivers may vary. Additionally, a single device may include
reflective
and/or transmissive PPG systems 235.
[0070] The PPG system 235 illustrated in FIG. 2 may include a reflective
PPG system
235 in some implementations. In these implementations, the PPG system 235 may
include a
centrally located optical receiver (e.g., at the bottom of the ring 104) and
two optical
transmitters located on each side of the optical receiver. In this
implementation, the PPG
system 235 (e.g., optical receiver) may generate the PPG signal based on light
received from
one or both of the optical transmitters. In other implementations, other
placements,
combinations, and/or configurations of one or more optical transmitters and/or
optical
receivers are contemplated.
[0071] The processing module 230-a may control one or both of the optical
transmitters
to transmit light while sampling the PPG signal generated by the optical
receiver. In some
implementations, the processing module 230-a may cause the optical transmitter
with the
stronger received signal to transmit light while sampling the PPG signal
generated by the
optical receiver. For example, the selected optical transmitter may
continuously emit light
while the PPG signal is sampled at a sampling rate (e.g., 250 Hz).
[0072] Sampling the PPG signal generated by the PPG system 235 may result
in a pulse
waveform, which may be referred to as a "PPG." The pulse waveform may indicate
blood
pressure vs time for multiple cardiac cycles. The pulse waveform may include
peaks that
indicate cardiac cycles. Additionally, the pulse waveform may include
respiratory induced
variations that may be used to determine respiration rate. The processing
module 230-a may
store the pulse waveform in memory 215 in some implementations. The processing
module
230-a may process the pulse waveform as it is generated and/or from memory 215
to
determine user physiological parameters described herein.
[0073] The processing module 230-a may determine the user's heart rate
based on the
pulse waveform. For example, the processing module 230-a may determine heart
rate (e.g., in
beats per minute) based on the time between peaks in the pulse waveform. The
time between
peaks may be referred to as an interbeat interval (IBI). The processing module
230-a may
store the determined heart rate values and IBI values in memory 215.
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[0074] The processing module 230-a may determine HRV over time. For
example, the
processing module 230-a may determine HRV based on the variation in the IBls.
The
processing module 230-a may store the HRV values over time in the memory 215.
Moreover,
the processing module 230-a may determine the user's respiratory rate over
time. For
example, the processing module 230-a may determine respiratory rate based on
frequency
modulation, amplitude modulation, or baseline modulation of the user's IBI
values over a
period of time. Respiratory rate may be calculated in breaths per minute or as
another
breathing rate (e.g., breaths per 30 seconds). The processing module 230-a may
store user
respiratory rate values over time in the memory 215.
[0075] The ring 104 may include one or more motion sensors 245, such as one
or more
accelerometers (e.g., 6-D accelerometers) and/or one or more gyroscopes
(gyros). The motion
sensors 245 may generate motion signals that indicate motion of the sensors.
For example,
the ring 104 may include one or more accelerometers that generate acceleration
signals that
indicate acceleration of the accelerometers. As another example, the ring 104
may include
one or more gyro sensors that generate gyro signals that indicate angular
motion (e.g.,
angular velocity) and/or changes in orientation. The motion sensors 245 may be
included in
one or more sensor packages. An example accelerometer/gyro sensor is a Bosch
BM1160
inertial micro electro-mechanical system (MEMS) sensor that may measure
angular rates and
accelerations in three perpendicular axes.
[0076] The processing module 230-a may sample the motion signals at a
sampling rate
(e.g., 50Hz) and determine the motion of the ring 104 based on the sampled
motion signals.
For example, the processing module 230-a may sample acceleration signals to
determine
acceleration of the ring 104. As another example, the processing module 230-a
may sample a
gyro signal to determine angular motion. In some implementations, the
processing module
230-a may store motion data in memory 215. Motion data may include sampled
motion data
as well as motion data that is calculated based on the sampled motion signals
(e.g.,
acceleration and angular values).
[0077] The ring 104 may store a variety of data described herein. For
example, the ring
104 may store temperature data, such as raw sampled temperature data and
calculated
temperature data (e.g., average temperatures). As another example, the ring
104 may store
PPG signal data, such as pulse waveforms and data calculated based on the
pulse waveforms
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(e.g., heart rate values, IBI values, HRV values, and respiratory rate
values). The ring 104
may also store motion data, such as sampled motion data that indicates linear
and angular
motion.
[0078] The ring 104, or other computing device, may calculate and store
additional
values based on the sampled/calculated physiological data. For example, the
processing
module 230 may calculate and store various metrics, such as sleep metrics
(e.g., a Sleep
Score), activity metrics, and readiness metrics. In some implementations,
additional
values/metrics may be referred to as "derived values." The ring 104, or other
computing/wearable device, may calculate a variety of values/metrics with
respect to motion.
Example derived values for motion data may include, but are not limited to,
motion count
values, regularity values, intensity values, metabolic equivalence of task
values (METs), and
orientation values. Motion counts, regularity values, intensity values, and
METs may indicate
an amount of user motion (e.g., velocity/acceleration) over time. Orientation
values may
indicate how the ring 104 is oriented on the user's finger and if the ring 104
is worn on the
left hand or right hand.
[0079] In some implementations, motion counts and regularity values may be
determined
by counting a number of acceleration peaks within one or more periods of time
(e.g., one or
more 30 second to 1 minute periods). Intensity values may indicate a number of
movements
and the associated intensity (e.g., acceleration values) of the movements. The
intensity values
may be categorized as low, medium, and high, depending on associated threshold
acceleration values. METs may be determined based on the intensity of
movements during a
period of time (e.g., 30 seconds), the regularity/irregularity of the
movements, and the
number of movements associated with the different intensities.
[0080] In some implementations, the processing module 230-a may compress
the data
stored in memory 215. For example, the processing module 230-a may delete
sampled data
after making calculations based on the sampled data. As another example, the
processing
module 230-a may average data over longer periods of time in order to reduce
the number of
stored values. In a specific example, if average temperatures for a user over
one minute are
stored in memory 215, the processing module 230-a may calculate average
temperatures over
a five minute time period for storage, and then subsequently erase the one
minute average
temperature data. The processing module 230-a may compress data based on a
variety of
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factors, such as the total amount of used/available memory 215 and/or an
elapsed time since
the ring 104 last transmitted the data to the user device 106.
[0081] Although a user's physiological parameters may be measured by
sensors included
on a ring 104, other devices may measure a user's physiological parameters.
For example,
although a user's temperature may be measured by a temperature sensor 240
included in a
ring 104, other devices may measure a user's temperature. In some examples,
other wearable
devices (e.g., wrist devices) may include sensors that measure user
physiological parameters.
Additionally, medical devices, such as external medical devices (e.g.,
wearable medical
devices) and/or implantable medical devices, may measure a user's
physiological parameters.
One or more sensors on any type of computing device may be used to implement
the
techniques described herein.
[0082] The physiological measurements may be taken continuously throughout
the day
and/or night. In some implementations, the physiological measurements may be
taken during
104 portions of the day and/or portions of the night. In some implementations,
the
physiological measurements may be taken in response to determining that the
user is in a
specific state, such as an active state, resting state, and/or a sleeping
state. For example, the
ring 104 can make physiological measurements in a resting/sleep state in order
to acquire
cleaner physiological signals. In one example, the ring 104 or other
device/system may detect
when a user is resting and/or sleeping and acquire physiological parameters
(e.g.,
temperature) for that detected state. The devices/systems may use the
resting/sleep
physiological data and/or other data when the user is in other states in order
to implement the
techniques of the present disclosure.
[0083] In some implementations, as described previously herein, the ring
104 may be
configured to collect, store, and/or process data, and may transfer any of the
data described
herein to the user device 106 for storage and/or processing. In some aspects,
the user device
106 includes a wearable application 250, an operating system (OS), a web
browser
application (e.g., web browser 280), one or more additional applications, and
a GUI 275. The
user device 106 may further include other modules and components, including
sensors, audio
devices, haptic feedback devices, and the like. The wearable application 250
may include an
example of an application (e.g., "app") that may be installed on the user
device 106. The
wearable application 250 may be configured to acquire data from the ring 104,
store the
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acquired data, and process the acquired data as described herein. For example,
the wearable
application 250 may include a user interface (UI) module 255, an acquisition
module 260, a
processing module 230-b, a communication module 220-b, and a storage module
(e.g.,
database 265) configured to store application data.
[0084] The various data processing operations described herein may be
performed by the
ring 104, the user device 106, the servers 110, or any combination thereof For
example, in
some cases, data collected by the ring 104 may be pre-processed and
transmitted to the user
device 106. In this example, the user device 106 may perform some data
processing
operations on the received data, may transmit the data to the servers 110 for
data processing,
or both. For instance, in some cases, the user device 106 may perform
processing operations
that require relatively low processing power and/or operations that require a
relatively low
latency, whereas the user device 106 may transmit the data to the servers 110
for processing
operations that require relatively high processing power and/or operations
that may allow
relatively higher latency.
[0085] In some aspects, the ring 104, user device 106, and server 110 of
the system 200
may be configured to evaluate sleep patterns for a user. In particular, the
respective
components of the system 200 may be used to collect data from a user via the
ring 104, and
generate one or more scores (e.g., Sleep Score, Readiness Score) for the user
based on the
collected data. For example, as noted previously herein, the ring 104 of the
system 200 may
be worn by a user to collect data from the user, including temperature, heart
rate, HRV, and
the like. Data collected by the ring 104 may be used to determine when the
user is asleep in
order to evaluate the user's sleep for a given "sleep day." In some aspects,
scores may be
calculated for the user for each respective sleep day, such that a first sleep
day is associated
with a first set of scores, and a second sleep day is associated with a second
set of scores.
Scores may be calculated for each respective sleep day based on data collected
by the ring
104 during the respective sleep day. Scores may include, but are not limited
to, Sleep Scores,
Readiness Scores, and the like.
[0086] In some cases, "sleep days" may align with the traditional calendar
days, such that
a given sleep day runs from midnight to midnight of the respective calendar
day. In other
cases, sleep days may be offset relative to calendar days. For example, sleep
days may run
from 6:00 pm (18:00) of a calendar day until 6:00 pm (18:00) of the subsequent
calendar day.
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In this example, 6:00 pm may serve as a "cut-off time," where data collected
from the user
before 6:00 pm is counted for the current sleep day, and data collected from
the user after
6:00 pm is counted for the subsequent sleep day. Due to the fact that most
individuals sleep
the most at night, offsetting sleep days relative to calendar days may enable
the system 200 to
evaluate sleep patterns for users in such a manner that is consistent with
their sleep schedules.
In some cases, users may be able to selectively adjust (e.g., via the GUI) a
timing of sleep
days relative to calendar days so that the sleep days are aligned with the
duration of time that
the respective users typically sleep.
[0087] In some implementations, each overall score for a user for each
respective day
(e.g., Sleep Score, Readiness Score) may be determined/calculated based on one
or more
"contributors," "factors," or "contributing factors." For example, a user's
overall Sleep Score
may be calculated based on a set of contributors, including: total sleep,
efficiency,
restfulness, REM sleep, deep sleep, latency, timing, or any combination
thereof The Sleep
Score may include any quantity of contributors. The "total sleep" contributor
may refer to the
sum of all sleep periods of the sleep day. The "efficiency" contributor may
reflect the
percentage of time spent asleep compared to time spent awake while in bed, and
may be
calculated using the efficiency average of long sleep periods (e.g., primary
sleep period) of
the sleep day, weighted by a duration of each sleep period. The "restfulness"
contributor may
indicate how restful the user's sleep is, and may be calculated using the
average of all sleep
periods of the sleep day, weighted by a duration of each period. The
restfulness contributor
may be based on a "wake up count" (e.g., sum of all the wake-ups (when user
wakes up)
detected during different sleep periods), excessive movement, and a "got up
count" (e.g., sum
of all the got-ups (when user gets out of bed) detected during the different
sleep periods).
[0088] The "REM sleep" contributor may refer to a sum total of REM sleep
durations
across all sleep periods of the sleep day including REM sleep. Similarly, the
"deep sleep"
contributor may refer to a sum total of deep sleep durations across all sleep
periods of the
sleep day including deep sleep. The "latency" contributor may signify how long
(e.g.,
average, median, longest) the user takes to go to sleep, and may be calculated
using the
average of long sleep periods throughout the sleep day, weighted by a duration
of each period
and the number of such periods (e.g., consolidation of a given sleep stage or
sleep stages may
be its own contributor or weight other contributors). Lastly, the "timing"
contributor may
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refer to a relative timing of sleep periods within the sleep day and/or
calendar day, and may
be calculated using the average of all sleep periods of the sleep day,
weighted by a duration
of each period.
[0089] By way of another example, a user's overall Readiness Score may be
calculated
based on a set of contributors, including: sleep, sleep balance, heart rate,
HRV balance,
recovery index, temperature, activity, activity balance, or any combination
thereof The
Readiness Score may include any quantity of contributors. The "sleep"
contributor may refer
to the combined Sleep Score of all sleep periods within the sleep day. The
"sleep balance"
contributor may refer to a cumulative duration of all sleep periods within the
sleep day. In
particular, sleep balance may indicate to a user whether the sleep that the
user has been
getting over some duration of time (e.g., the past two weeks) is in balance
with the user's
needs. Typically, adults need 7-9 hours of sleep a night to stay healthy,
alert, and to perform
at their best both mentally and physically. However, it is normal to have an
occasional night
of bad sleep, so the sleep balance contributor takes into account long-term
sleep patterns to
determine whether each user's sleep needs are being met. The "resting heart
rate" contributor
may indicate a lowest heart rate from the longest sleep period of the sleep
day (e.g., primary
sleep period) and/or the lowest heart rate from naps occurring after the
primary sleep period.
[0090] Continuing with reference to the "contributors" (e.g., factors,
contributing factors)
of the Readiness Score, the "HRV balance" contributor may indicate a highest
HRV average
from the primary sleep period and the naps happening after the primary sleep
period. The
HRV balance contributor may help users keep track of their recovery status by
comparing
their HRV trend over a first time period (e.g., two weeks) to an average HRV
over some
second, longer time period (e.g., three months). The "recovery index"
contributor may be
calculated based on the longest sleep period. Recovery index measures how long
it takes for a
user's resting heart rate to stabilize during the night. A sign of a very good
recovery is that
the user's resting heart rate stabilizes during the first half of the night,
at least six hours
before the user wakes up, leaving the body time to recover for the next day.
The "body
temperature" contributor may be calculated based on the longest sleep period
(e.g., primary
sleep period) or based on a nap happening after the longest sleep period if
the user's highest
temperature during the nap is at least 0.5 C higher than the highest
temperature during the
longest period. In some aspects, the ring may measure a user's body
temperature while the
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user is asleep, and the system 200 may display the user's average temperature
relative to the
user's baseline temperature. If a user's body temperature is outside of their
normal range
(e.g., clearly above or below 0.0), the body temperature contributor may be
highlighted (e.g.,
go to a "Pay attention" state) or otherwise generate an alert for the user.
[0091] In some aspects, the system 200 may support techniques for
determining heart rate
data of a user based on physiological data (e.g., motion data) collected by a
wearable device.
In some aspects, the ring 104, user device 106, and servers 110 of the system
200 may be
configured to determine heart rate data of a user during periods of motion,
activity, exercise,
etc. In particular, the respective components of the system 200 may be used to
determine
heart rate data (e.g., exercise heart rate data) of a user based on
physiological data of the user
(e.g., motion). For example, the respective components of the system 200 may
collect
physiological data of the user that the ring 104-a (or other components of
system 200) may
use to select actual heart rate data and remove or ignore motion artifacts
that may result in
false heart rate measurements. As such, the physiological data may be obtained
by leveraging
sensors on the ring 104 of the system 200.
[0092] For example, as noted previously herein, the ring 104 of the system
200 may be
worn by a user to collect data from the user, including temperature, heart
rate, movement, and
the like. The ring 104 of the system 200 may collect the physiological data
from the user
based on arterial blood flow. Physiological data collected by the ring 104 may
be used to
determine false or erroneous heart rate data that is due to motion (e.g.,
motion artifacts),
rather than actual blood flow and accordingly, may be used to determine the
actual heart rate
data.
[0093] For instance, in some implementations, acquired PPG data and motion
data may
be inputted into a machine learning model (e.g., heuristic-based model, deep
learning model,
regression-based model) that is configured to determine a heart rate for the
user. In such
cases, the machine learning model may be configured to differentiate between
candidate heart
rate measurements that are attributable to motion artifacts from candidate
heart rate
measurements that are indicative of the user's actual heart rate.
Additionally, or alternatively,
the machine learning model may be configured to identify time-domain and/or
frequency-
domain features within the received PPG data and motion data (which may be
used to
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identify candidate heart rate measurements), and may be configured to
determine or estimate
a heart rate for the user based on the identified features.
[0094] The procedure for determining heart rate data (e.g., exercise heart
rate data) may
be further shown and described with reference to FIGs. 3 and 4.
[0095] FIG. 3 illustrates an example of a heart rate determination
procedure 300 that
supports techniques for measuring heart rate in accordance with aspects of the
present
disclosure. The heart rate determination procedure 300 may implement, or be
implemented
by, aspects of the system 100, system 200, or a combination thereof For
example, in some
implementations, the heart rate determination procedure 300 may result in
heart rate data
(e.g., active heart rate data, exercise heart rate data) that may be displayed
to a user via the
GUI 275 of the user device 106, as shown in FIG. 2.
[0096] As described herein, a system (e.g., system 200), or a portion of
system 200, such
as a wearable device (e.g., a ring 104), may identify heart rate data of a
user from a set of
heart rate data, where the heart rate data may include motion artifacts. In
some cases, a
wearable device may detect heart rate data of a user in accordance with the
techniques
described herein during periods of activity, motion, exercise, etc. In some
cases, the
techniques for determining heart rate data described herein may be utilized
for heart rate
detections other than active heart rate detections, or for all heart rate
detections (e.g., despite
whether the user is active or exercising). Accordingly, the heart rate
determination procedure
300 may not be limited to determining heart rate of a user during exercise,
motion, activity,
etc. In some cases, the wearable device may detect that a user is performing
an activity (e.g.,
an activity that meets a movement threshold, an activity resulting in a heart
rate that meets a
heart rate threshold), exercising, etc. and may perform the heart rate
determination procedure
300 described herein based on detecting the activity. In some cases, a user
may inform the
wearable device that the user is performing such an activity or otherwise
prompt the wearable
device to perform the heart rate determination procedure 300 described herein.
[0097] At 305, the wearable device may measure motion data (e.g.,
acceleration data)
associated with the user. In some cases, the wearable device may measure
motion data
constantly, periodically (e.g., in accordance with a periodicity), based on
activity, time of
day, etc. For example, the wearable device may be configured to measure motion
data (e.g., a
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motion data point) every one second. In some cases, the wearable device may be
configured
to obtain the motion data and perform the procedure described herein based on
the user of the
wearable device performing an activity (e.g., such as an activity that meets a
movement
threshold), exercising, etc. During such an activity, the wearable device may
be configured to
measure the motion data in accordance with a periodicity, such as every one
second for the
duration of the activity.
[0098] In some cases, the wearable device may measure motion for a
configured
duration, where the wearable device may be configured with or receive an
indication of the
configured duration or may determine the configured duration. In some cases,
the duration
may be based on a duration of an activity, exercise, etc. being performed by
the user. To
measure motion data, the wearable device may utilize one or more motion
sensors on the
wearable device (e.g., motion sensors 245). In some cases, the motion data may
refer to
acceleration data. In such cases, the motion sensors on the wearable device
may refer to
accelerometers (e.g., 3D accelerometers) that are capable of detecting a
user's acceleration,
such as 3D acceleration. The wearable device may obtain motion data in the x,
y, and z axis,
where each axis may be referred to as a different motion channel. Accordingly,
the wearable
device may obtain motion data over three motion channels. In some cases, the
accelerometers
may measure the user's acceleration at 50 Hz, or some other frequency.
[0099] At 310, the wearable device, the user device, the servers, or any
combination
thereof, may preprocess the motion data. For example, the wearable device may
preprocess
the 3D acceleration data (e.g., raw data) to obtain processed acceleration
data. The wearable
device may preprocess each motion channel separately. In some cases, the
preprocessing may
include removing outliers, erroneous data, noise, or a combination thereof
from the motion
data.
[0100] At 315, the wearable device, the user device, the servers, or any
combination
thereof, may perform a multipath merger procedure. As described, the wearable
device may
obtain motion data via three channels: x, y, and z. Accordingly, the wearable
device may
input the data associated with the three separate channels into a multipath
merger module to
obtain a single channel (e.g., a single set of motion data). In some cases,
the wearable device
may be configured to select one motion channel from the set of motion channels
(e.g., select
the x-axis motion channel, the y-axis motion channel, or the z-axis motion
channel). The
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selection may be based on quality, such that the wearable device may select
the channel
associated with the highest quality, the least number of outliers, the least
amount of noise, or
some combination thereof
[0101] In some cases, the wearable device, the user device, the servers, or
any
combination thereof, may combine two or more motion channels to generate
aggregate or
composite motion data using any mathematical operation, including an averaging
operation, a
weighted averaging operation, and the like. For example, the wearable device
may average at
least two of the channels. In some cases, the wearable device may average the
two channels
associated with the highest quality (e.g., associated with the least amount of
outliers, noise,
etc.). In some cases, the wearable device may be configured to average any of
the channels if
each of the channels meets a quality threshold. In some cases, the wearable
device may be
configured to average a particular number of channels. For example, the
wearable device may
be configured to average all three of the motion channels to obtain the
average channel.
Accordingly, the output of the multipath merger module is a single motion
channel. At 320,
the wearable device, the user device, the servers, or any combination thereof,
may input the
motion data (e.g., the preprocessed and merged/composite motion data) into a
time domain
processor module. The time domain processor module may calculate an intensity
associated
with the motion data (e.g., motion intensity), a change in motion intensity
(e.g., intensity
change rate), or both. The time domain processor module may output motion
intensity data
over time, or change in motion intensity over time. In some cases, calculating
the motion
intensity and/or intensity change rate may include calculating an absolute
value of the motion
data, and calculating the mean of the absolute value. In some cases, the
wearable device may
input time-domain motion data into the time domain processor module and the
module may
output frequency-domain motion data, or vice versa. In other words, components
of the
system 200 may convert the motion data into a time/frequency domain
representation of the
motion data..
[0102] At 325, the wearable device may obtain PPG data. The wearable device
may
sample the PPG at a set of conditions, such as frequency (e.g., 50 Hz). In
some cases, the
wearable device may measure PPG data constantly, periodically (e.g., in
accordance with a
periodicity), based on activity, time of day, etc. For example, the wearable
device may be
configured to measure PPG data (e.g., collect a PPG data point) every one
second. In some
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cases, the wearable device may be configured to obtain the PPG data and
perform the
procedure described herein based on the user of the wearable device performing
an activity
(e.g., such as during a time that an activity meets a movement threshold),
exercising, etc.
During such an activity, the wearable device may be configured to measure the
PPG data in
accordance with a periodicity, such as every one second for the duration of
the activity.
[0103] In some cases, the wearable device may measure motion for a
configured
duration, where the wearable device may be configured with or receive an
indication of the
configured duration or may determine the configured duration. In some cases,
the duration
may be based on a duration of an activity, exercise, etc. being performed by
the user. To
measure PPG data, the wearable device may sample PPG data for the user using
one or more
sets of PPG sensors, where each set of PPG sensors includes at least one light
source (e.g.,
LED) and at least one photodetector. For example, a wearable device may
include a first pair
of PPG sensors including a first LED and a first photodetector, and a second
pair of PPG
sensors including a second LED and a second photodetector. In other words, the
wearable
device may include two separate "channels" for acquiring PPG data (e.g., two
separate PPG
signals from the two respective pairs of sensors). In some cases, the wearable
device may
sample PPG data using the first and second pairs of PPG sensors
simultaneously.
Additionally, or alternatively, the wearable device may sample PPG data using
the first and
second sets of PPG sensors sequentially. For instance, the wearable device may
sequentially
control an activation state of the first pair of PPG sensors and the second
pair of PPG sensors
(e.g., first pair is in an active state when the second pair is in an inactive
state, and vice
versa).
[0104] In some implementations, the wearable device may sample PPG using
components across multiple sets of sensors. For example, the wearable device
may obtain
PPG data using the first LED and the second photodetector, or using the second
LED and the
first photodetector, or both. In some cases, the first LED and the second
photodetector may
be opposite each other on the wearable device. Accordingly, in a wearable
device that
includes two pairs of PPG sensors, the wearable device may obtain four PPG
signals (e.g.,
four PPG channels). Sequentially activating separate sets/pairs of PPG sensors
may improve
the quality and accuracy of each respective PPG signal, reduce interference,
and may lead to
more accurate heart rate measurements.
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[0105] At 330, the wearable device, the user device, the servers, or any
combination
thereof, may preprocess the PPG data. For example, the wearable device may
filter the PPG
data, such as to remove erroneous samples, outliers, noise, etc. The wearable
device may
preprocess each PPG channel separately.
[0106] At 335, the wearable device, the user device, the servers, or any
combination
thereof, may perform a multipath merger procedure. As described, the wearable
device may
monitor PPG signals from multiple sets of sensors. For example, the wearable
device may
obtain four PPG signals (e.g., PPG signals collected via four separate
channels). Accordingly,
the wearable device may input the data associated with the four separate
channels into a
multipath merger module to obtain a single channel (e.g., a single set of PPG
data).
[0107] In some cases, the wearable device may be configured to select one
PPG channel
from the set of PPG channels. The selection may be based on quality, such that
the wearable
device may select the channel associated with the highest quality, least
number of outliers,
noise, etc. For example, in the context of a ring wearable device, each of the
respective
sensors used for PPG sampling (e.g., LEDs, photodetectors) may be positioned
at a different
radial position along an inner circumference of the ring. Accordingly, one of
the PPG signals
from one or more of the multiple sets of PPG sensors may be more reliable than
the PPG
signals obtained from other of the multiple sensors, such as due to the
positions of the sensors
around the wearable device and in relation to the user, a relative quality of
skin contact with
each respective sensor or LED, and the like. For example, a sensor located on
the underside
(e.g., palm side) of a user's finger may result in more accurate PPG data than
a sensor located
over the bone of the finger (e.g., on the backhand side of the finger). By way
of another
example, a relative positioning of a wearable device may result in less skin
contact at one
first sensor as compared to a second sensor, and may therefore result in lower
quality PPG
data as compared to the second sensor. As such, the output of the multipath
merger module
may be based on a quality of the signal produced by each pair of sensors.
[0108] In some cases, the wearable device may combine two or more PPG
channels/signals to generate aggregate or composite PPG data using any
mathematical
operation, including an averaging operation, a weighted averaging operation,
and the like. For
example, the wearable device may average at least two of the PPG channels. In
some cases,
the wearable device may average the top two or three PPG channels associated
with the
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highest quality (e.g., associated with the least amount of outliers, noise,
etc.). In some cases,
the wearable device may be configured to average any number of the channels if
each of the
channels meets a quality threshold. In some cases, the wearable device may be
configured to
average a particular number of channels. For example, the wearable device may
be
configured to average all four of the PPG channels to obtain the average PPG
channel.
Accordingly, the output of the multipath merger module is a single PPG
signal/channel (e.g.,
composite PPG signal),In some aspects, components of the system 200 may
convert the PPG
data (e.g., composite PPG signal) into a time/frequency domain representation
of the PPG
data.
[0109] At 340, the wearable device, the user device, the servers, or any
combination
thereof, may perform heart rate detection. The wearable device may estimate
heart rate of the
user based on the obtained PPG signal. Each time that a user's heart beats,
blood is pumped
out to the arteries located in the hands and fingers. PPG sensors (e.g., of
the PPG system 235
as described with reference to FIG. 2) in the wearable device are able to
detect these changes
in blood flow and volume using light reflection and absorption. Each pulse
causes the arteries
in the finger to alternate between dilating and constricting (e.g., swelling
and contracting). By
shining a light on the skin, such as via LEDs, changes in light reflected back
from the
wavering volume of red blood cells in the arteries is measured. From here, PPG
can represent
these blood flow changes through a visual waveform that represents the
activity of the user's
heart, and thus heart rate. In some cases, the wearable device may determine
the heart rate of
the user based on the PPG signal satisfying a quality threshold, or based on
portions of the
PPG signal satisfying a quality threshold. For example, the wearable device
may determine
one or more portions of the PPG signal that actively represent the heart rate
of the user.
[0110] As described herein, some activities may produce a PPG signal that
may be
misinterpreted as a heart rate signal. For example, exercises such as running,
jumping, etc.
may cause motion artifacts. The optical nature of PPG sensors may render the
PPG
measurements susceptible to motion artifacts from variable and discontinuous
contact
between the device and skin. Motion artifacts are typically caused by the
change of blood
flow velocity induced by the motion of the exercise or the relative movement
between PPG
sensors and skin of the user. For example, some motions may result in a
periodic pressure
between the wearable device (e.g., the sensors of the wearable device) and the
part of the user
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that the wearable device is positioned, and as such may apply periodic
pressure to blood
vessels. The periodic pressure may cause the blood vessels to periodically
constrict and
dilate. Accordingly, the wearable device may detect such constriction and
dilation of the
vessels as an artificial PPG signal (e.g., motion artifacts) because the
constriction and dilation
were due to the motion of the user, and not due to the heart rate of the user.
[0111] Accordingly, to perform heart rate detection the wearable device may
compare the
PPG data and the motion data. For example, the wearable device may detect
multiple strong
PPG signals in a given time interval. The multiple PPG signals may represent
multiple
"candidate" heart rates for the user within the given time interval, where one
of the candidate
heart rates represents the user's actual heart rate, and other candidate heart
rates may be
attributable to motion (e.g., artificial heart rates, or motion artifacts).
Accordingly, the system
200 may be configured to identify which candidate PPG signal represents the
user's actual
heart rate, and may be configured to eliminate or otherwise disregard PPG
signals that are
attributable to motion.
[0112] In some aspects, the system 200 may use the motion data such as the
merged
motion data, the motion intensity data, intensity change rate data, or a
combination thereof to
determine which of the multiple strong PPG signals (e.g., candidate PPG
signals) are due to
motion, and which PPG signal represents the user's actual heart rate. For
example, the system
200 may determine that the motion data collected during a given time interval
exhibits a
relative periodicity (e.g., frequency pattern) which is similar or the same as
a
periodicity/frequency pattern exhibited by one of the candidate heart rates.
In this example,
the system 200 may identify the candidate heart rate that exhibits a similar
periodicity/frequency pattern as the motion data as a motion artifact, and may
therefore
disregard the identified candidate heart rate/motion artifact for the purposes
of identifying the
user's actual heart rate. In some cases, the wearable device may analyze the
motion data and
the PPG data in units such that the wearable device may determine at which
data points other
PPG signals overlap with motion signals. The wearable device may remove the
PPG signals
that are overlapping with motion signals, or are otherwise attributable to
motion (e.g., remove
motion artifacts).
[0113] However, blood vessels of healthy humans are soft and elastic, and
they have
harmonic frequencies when waves travel through them. Harmonic frequencies are
an integer
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multiple of the fundamental frequency. In some cases, some users (e.g.,
athletes) may
exercise based on pulse harmonic frequency such that the user may match their
cadence to a
harmonic frequency of their pulse. Accordingly, in some cases, the wearable
device may be
unable to remove all PPG signals that are overlapping with a motion signal
because it could
be that the PPG signal that is overlapping is an actual heart rate signal to
which the user
accurately matched their cadence. Accordingly, upon determining overlapping
PPG and
motion signals, the wearable device may additionally analyze the data to
determine whether
to remove the overlapping PPG signals.
[0114] In some aspects, the system 200 may identify one or more candidate
heart rates
(e.g., candidate heart rate measurements) as motion artifacts based on a
comparison of data
trends between the candidate heart rate measurements and the motion data. For
example, if a
periodicity of motion data increases, the system 200 may identify a candidate
heart rate
measurement that shows an increasing heart rate as a motion artifact due to
the comparison in
trends between the motion data and the candidate heart rate data. In some
cases, the wearable
device may determine whether the motion data is steady, increasing,
decreasing, etc. (e.g.,
trends in the heart rate data) and may use such information to determine
dynamic heart rate
limits (e.g., ranges). The wearable device may use the heart rate limits
(e.g., expected range
of human heart rates) to further determine which of the multiple PPG signals
may be
removed and determine the true heart rate signal. In other words, the wearable
device may
compare the PPG signals to the heart rate limits or ranges and may eliminate
the PPG signals
that do not fall within the expected heart rate limits or ranges (e.g.,
candidate heart rates with
a periodicity/frequency pattern that falls outside an expected range of human
heart rates may
be labeled or identified as motion artifacts).
[0115] Upon determining the true heart rate signal (e.g., identifying the
candidate PPG
signal that represents the user's actual heart rate), the wearable device may
detect gaps in the
heart rate data. For example, with respect to a ring device, as the ring
rotates, the PPG sensors
located around the ring may also rotate and in some cases may be unable to
detect a signal
(e.g., such as due to the sensor being located over bone). Accordingly, in
some cases, the
wearable device may identify heart rate data on either side of the gap to
interpolate the heart
rate data in the gap. For example, the wearable device may analyze heart rate
data during a
first time (e.g., just prior to the gap), and analyze heart rate data during a
second time (e.g.,
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just after the gap). Based on the analysis, such as the trend, intensity, or
both of the heart rate
data and/or motion data during the first and second times, the wearable device
may
interpolate the heart rate data in the gap. For example, if an intensity of
the motion data
increased during the gap, it may be expected that the user's heart rate would
also increase due
to the increased motion intensity. As such, the system 200 may perform the
interpolation
across the gap to represent an increased heart rate due to the corresponding
increase in
motion intensity.
[0116] In additional or alternative implementations, the system 200 may
utilize an
"estimator" or a machine learning model/algorithm (e.g., heuristic-based
model, deep
learning model, regression-based model) to perform the heart rate detection at
340. In
particular, the motion data collected/processed at 305 through 320, and the
PPG data
collected and processed at 325 through 335, may be inputted into a machine
learning model,
where the machine learning model is configured to determine a heart rate for
the user (e.g.,
select or estimate a candidate heart rate from the set of candidate heart
rates).
[0117] In some cases, in order to train a machine learning model for heart
rate detection,
wearable devices may acquire physiological data (e.g., PPG data, motion data)
from one or
more users (such as while the users are exercising), and ECG-based devices may
be used to
acquire heart rate data for the respective users (e.g., during the same time
intervals). In such
cases, the ECG-based heart rate data may serve as "ground truth" heart rate
measurements
that are used to train the machine learning model.
[0118] In this example, the physiological data (e.g., PPG data, heart rate
data) may be
inputted into the machine learning model, where the machine learning model is
trained to
generate or output the ECG-based heart rate data based on the received
physiological data
acquired from the wearable device(s). In other words, the ECG-based data may
be considered
a desired output of the model, where the machine learning model is trained to
match (or
closely estimate) the ECG-based heart rate data based on receiving the
physiological data. As
such, the machine learning model may be trained to receive PPG data and motion
data as
inputs, and generate heart rate measurements/estimations as outputs.
[0119] In some cases, the system 200 may train multiple versions of a
machine learning
model, such as for different demographics of users (e.g., different age
groups, varying levels
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of activity/performance, different skin tones, etc.), for users with varying
medical conditions,
and the like. In this regard, different models which are tailored to different
demographics of
users may be used to further fine-tune the ability of the respective models to
perform heart
rate detection. For example, the system 200 may acquire physiological data
from a user who
is an avid runner, and may utilize a machine learning model trained on data
from other
runners to perform heart rate detection for the user.
[0120] After training the machine learning model(s), the machine learning
model may be
used to perform "live" heart rate measurements for users based on
physiological data (e.g.,
PPG data, motion data) acquired from the wearable device. For example,
time/frequency
domain motion data and time/frequency PPG data illustrated in FIG. 3 may be
inputted into
the machine learning model at 340, where the machine learning model is
configured to output
a heart rate measurement/estimation based on the received data.
[0121] For instance, the machine learning model may be configured to
differentiate
between candidate heart rate measurements that are attributable to motion
artifacts from
candidate heart rate measurements that are indicative of the user's actual
heart rate. In such
cases, the machine learning model may be configured to select (e.g., identify,
estimate) a
candidate heart rate measurement, and determine a heart rate for the user
based on the
selected/estimated candidate heart rate measurements. For instance, the
machine learning
model may be configured to identify PPG heart rate candidates (e.g., candidate
heart rate
measurements) within the received heart rate data, and estimate a heart rate
measurement
based on the identified candidate heart rate (e.g., by selecting one of the
candidate heart rate
measurements, modifying a candidate heart rate measurement, estimating a new
candidate
heart rate measurement, etc.).
[0122] By way of another example, the machine learning model may be
configured to
identify or extract time-domain and/or frequency-domain features within the
received PPG
data and motion data. In this example, the time-domain and/or frequency-domain
features
may be used as inputs to the machine learning model to determine/estimate
heart rate
measurements. For instance, time/frequency domain features extracted from
acquired
physiological data may include the candidate heart rate measurements, where
the machine
learning model is configured to receive the candidate heart rate measurements
as an input and
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estimate a heart rate measurement (e.g., select or modify a candidate heart
rate measurement)
as an output.
101231 At 345, the wearable device may output the true heart rate data. The
time series of
the heart rate data may be displayed in an application (e.g., a GUI 275).
Presentation of heart
rate data will be further shown and described with reference to FIG. 5, below.
101241 In some cases, steps 305 through 345 may be performed by the
wearable device, a
user device (e.g., user device 106), an application (e.g., an application of
user device 106), or
a combination thereof For example, a combination of the wearable device and a
user device
may perform steps 305 through 345, and an application may output the heart
rate data on the
user device. In some cases, steps 305 through 335 may be performed every n
seconds (e.g.,
such as every one second). Additionally, or alternatively, one or more aspects
of step 340
may be performed in accordance with a configured periodicity. For example, the
wearable
device may detect strong motion signals every x seconds (e.g., 5 seconds), and
detect strong
PPG signals evert y seconds (e.g., 15 seconds). The wearable device may be
configured to
perform at least steps 305 through 335 during the activity, exercise, etc. and
may be
configured to perform one or more aspects of steps 340 and 345 post-activity,
exercise, etc. In
some cases, the wearable device may be configured to perform one or more
aspects of steps
340 and 345 during the activity, exercise, etc.
[0125] FIG. 4 illustrates an example of a heart rate determination
procedure 400 that
supports techniques for measuring heart rate in accordance with aspects of the
present
disclosure. The heart rate determination procedure 400 may implement, or be
implemented
by, aspects of the system 100, system 200, or a combination thereof In some
cases, one or
more aspects of heart rate determination procedure 400 may be the same or
similar to one or
more aspects of heart rate determination procedure 300. In some
implementations, the heart
rate determination procedure 400 may result in heart rate data (e.g., active
heart rate data,
exercise heart rate data) that may be displayed to a user via the GUI 275 of
the user device
106, as shown in FIG. 2.
[0126] As described herein, a system, such a system 200, or a portion of
system 200, such
as a wearable device (e.g., a ring 104), may identify heart rate data of a
user from a set of
heart rate data, where the heart rate data may include motion artifacts. In
some cases, a
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wearable device may detect heart rate data of a user in accordance with the
techniques
described herein during periods of activity, motion, and exercise. In some
cases, the
techniques for determining heart rate data described herein may be utilized
for heart rate
detections other than active heart rate detections, or for all heart rate
detections (e.g., despite
whether the user is active or exercising). Accordingly, the heart rate
determination procedure
may not be limited to determining heart rate of a user during exercise,
motion, activity, etc.,
and may additionally or alternatively be used for determining heart rate
during periods of
rest, relaxation, mediation, and the like.
[0127] As described with reference to FIG. 3, the wearable device may
obtain PPG data.
To measure PPG data, the wearable device may sample PPG data for the user
using one or
more sets of PPG sensors, where each set of PPG sensor includes at least one
LED and at
least one photodetector. For example, in a wearable device that includes two
pairs of PPG
sensors, the wearable device may obtain four PPG signals (e.g., four PPG
channels). For
example, the wearable device may obtain PPG graphs 405-a, 405-b, 405-c, 405-d,
which
represent the PPG data obtained via the four different channels (e.g., the
four different PPG
sensor combinations).
[0128] The wearable device and/or other components of the system 200 may
preprocess
each PPG channel separately and subsequently may perform a multipath merger
procedure so
as to obtain a single PPG signal (e.g., composite PPG signal). In some cases,
the wearable
device may be configured to select one channel from the set of channels. The
selection may
be based on quality, such that the wearable device may select the channel
associated with the
highest quality, least number of outliers, noise, etc. In some cases, the
wearable device may
combine two or more PPG signals using one or more mathematical operations. For
example,
the wearable device may average at least two of the channels. In some cases,
the wearable
device may average the top two or three channels associated with the highest
quality (e.g.,
associated with the least amount of outliers, noise, etc.). In some cases, the
wearable device
may be configured to average any number of the channels if each of the
channels meets a
quality threshold. In some cases, the wearable device may be configured to
average a
particular number of channels, or a particular subset of channels. The
wearable device may be
configured to average all four of the PPG channels to obtain the average PPG
channel. In
some cases, determining the single PPG channel may be based on one or more
mathematical
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expressions. Accordingly, the output of the multipath merger module is a
single PPG channel,
such as PPG graph 405-e (e.g., a composite PPG signal).
[0129] Additionally, the wearable device may measure motion data (e.g.,
acceleration
data) associated with the user. To measure motion data, the wearable device
may utilize one
or more motion sensors on the wearable device (e.g., motion sensors 245). In
some cases, the
wearable device may obtain motion data in the x, y, and z axis, where each
axis may be
referred to as a different motion channel. Accordingly, the wearable device
may obtain
motion data over three motion channels. With reference to FIG. 4, graphs 410-
a, 410-b, and
410-c may represent motion data obtained in the x, y, and z axes,
respectively. For example,
graph 410-a may be associated with motion obtained in the x axis, graph 410-b
may be
associated with motion obtained in they axis, and graph 410-c may be
associated with
motion obtained in the z axis, for example. The wearable device may preprocess
each of the
channels, and then determine whether to combine one or more of the channels to
obtain a
single motion channel.
[0130] In some cases, the wearable device and/or other components of the
system 200
may be configured to select one motion channel from the set of channels. The
selection may
be based on quality, such that the wearable device may select the channel
associated with the
highest quality, least number of outliers, noise, etc. In some cases, the
wearable device may
combine two or more of the motion channels using one or more mathematical
operations. For
example, the wearable device may average at least two of the channels. In some
cases, the
wearable device may average the two channels associated with the highest
quality (e.g.,
associated with the least amount of outliers, noise, etc.). In some cases, the
wearable device
may be configured to average any of the channels if each of the channels meets
a quality
threshold. In some cases, the wearable device may be configured to average a
particular
number of channels, or a particular subset of channels. The wearable device
may be
configured to average all three of the motion channels to obtain the average
channel. In some
cases, determining the signal motion channel may be based on one or more
mathematical
expressions. Accordingly, upon selecting a single x, y, or z channel, or upon
merging two of
more of the channels, the wearable device may obtain a single motion graph 410-
d (e.g., a
composite motion signal).
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[0131] Upon determining a single PPG signal and motion signal, the wearable
device
may determine a heart rate of the user. However, as noted previously herein,
PPG data may
be sensitive to motion, where in some cases, some activities or exercises may
produce a PPG
signal that is not due to heart rate. As such, the PPG signal may represent a
false heart rate
425 (e.g., motion artifacts). For example, with reference to FIG. 4, graph 415
may depict an
actual heart rate 420 and a false heart rate 425. Accordingly, to provide the
user with accurate
heart rate data, the wearable device may perform steps 320, 340, and 345 as
described with
reference to FIG. 3 to detect an actual heart rate signal of the user. In
other words, the actual
heart rate 420 and the false heart rate 425 may include "candidate" heart rate
measurements,
and the system 200 may be configured to determine whether the PPG data
corresponding to
the actual heart rate 420 or the PPG data corresponding to the false heart
rate 425 accurately
represents the user's real heart rate.
[0132] Accordingly, the wearable device may identify one or more heart rate
measurements from the set of candidate heart rate measurements as motion
artifacts based at
least in part on a comparison of data trends between the set of candidate
heart rate
measurements (e.g., actual heart rate 420, false heart rate 425) and the
motion data. The
wearable device may select the first heart rate measurement from a subset of
the set of
candidate heart rate measurements that does not include the one or more heart
rate
measurements that were identified as motion artifacts. For example, the system
200 may
identify the false heart rate 435 as a motion artifact, and may therefore
select the actual heart
rate 420 from the set of candidate heart rate measurements as the heart rate
that reflects the
user's actual heart rate during the given time interval.
[0133] Identifying the one or more heart rate measurements as motion
artifacts may
include identifying the one or more heart rate measurements from the set of
candidate heart
rate measurements as motion artifacts based at least in part on the one or
more heart rate
measurements exhibiting a similar frequency pattern relative to the motion
data. As noted
previously herein, the system 200 may use the motion data such as the merged
motion data,
the motion intensity data, intensity change rate data, or a combination
thereof to determine
which of the candidate heart rate measurements/PPG signals are due to motion,
and which
heart rate measurement/PPG signal represents the user's actual heart rate. In
some cases, the
wearable device may analyze the motion data and the PPG data in units such
that the
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wearable device may determine at which data points other PPG signals overlap
with motion
signals. The wearable device may remove the PPG signals that are overlapping
with motion
signals, or are otherwise attributable to motion (e.g., remove motion
artifacts).
[0134] As noted previously herein, in some implementations, the system 200
may utilize
a machine learning model/algorithm (e.g., heuristic-based model, deep learning
model,
regression-based model) to perform the heart rate detection. In particular, a
machine learning
model may be trained to receive PPG data and motion data from a wearable
device, and
output a heart rate measurement/estimation based on the received data. For
example,
time/frequency domain PPG data (e.g., data including PPG heart rate
candidates/candidate
heart rates) and time/frequency domain motion data may be inputted into a
machine learning
model, where the machine learning model is configured to estimate/determine a
heart rate
measurement based on the received time/frequency domain PPG and motion data.
[0135] FIG. 5 illustrates an example of a GUI 500 that supports techniques
for measuring
heart rate in accordance with aspects of the present disclosure. The GUI 500
may implement,
or be implemented by, aspects of the system 100, system 200, heart rate
determination
procedure 300, heart rate determination procedure 400, or any combination
thereof For
example, the GUI 500 may include an example of the GUI 275 included within the
user
device 106 illustrated in FIG. 2.
[0136] The GUI 500 illustrates a series of application pages 505 that may
be displayed to
the user via the GUI 500 (e.g., GUI 275 illustrated in FIG. 2). The server 110
of system 200
may cause the GUI 500 of the user device 106 (e.g., mobile device) to display
an indication
of the heart rate data (e.g., via application page 505-a, or 505-b).
Accordingly, upon
determining heart rate data (e.g., as described with reference to FIGs. 3 and
4), the user may
be presented with the application page 505-a upon opening the wearable
application 250. As
shown in FIG. 5, the application page 505-a may display a heart rate graph 510-
a. The heart
rate graph 510-a may include a visual representation of how the user's heart
rate reacted to
different events and activities (e.g., exercise, sleep, rest, etc.).
[0137] In some cases, the heart rate graph 510-a may display the heart rate
of the user
over minutes, hours, days, etc. In some cases, the heart rate graph 510 may
display a
combination of daytime heart rate data and nighttime heart rate data (e.g.,
awake heart rate
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data and asleep heart rate data). Additionally, in some implementations, the
application page
505-a may display one or more scores (e.g., Sleep Score, Readiness Score 515,
Activity
Score, inactive time) for the user for the respective day (e.g., respective
sleep day), where the
one or more scores may be based on the heart rate data. By way of another
example, the heart
rate data may be used to update at least a subset of the factors for the
Readiness Score 515
(e.g., subset of sleep, sleep balance, HRV balance, recovery index, activity,
activity balance).
In some cases, application page 505-a may include an addition button 530 that
a user may
press to add additional information to the page, such as a workout, a moment,
a tag, etc.
[0138] Continuing with reference to FIG. 5, a user may be able to select
the heart rate
graph 510-a on the application page 505-a in order to view details associated
with the heart
rate, as shown in application page 505-b ("heart rate modal"). In other words,
tapping on the
heart rate graph 510-a shown on application page 505-a may cause the GUI 500
to display
application page 505-b so that the user may quickly and easily view the heart
rate of the user
over time. The application page 505-b may include a modal view including
details for the
heart rate. Heart rate graphs 510-a and 510-b may display the same or
different graph. For
example, the time scale may be the same or different. In some cases,
application page 505-b
may indicate a portion of the heart rate graph 510-b associated with increased
motion,
exercise, activity, etc., such as to explain a reason for a change in heart
rate. Application page
505-b may also include a day heart rate range 520-a, a relaxed heart rate
range 520-b, a
sleeping heart rate range 520-c, an exercise heart rate range 520-d, etc. The
individual ranges
may be on a per-hour basis, for example. In some cases, the application page
505-b may
display the heart rate data as HRV, resting heart rate, etc.
[0139] In some cases, a user may be able to select the exercise range 520-
d, or some other
aspect of application page 505-b associated with exercise, motion, activity,
etc. in order to
view details associated with the heart rate during such activity, as shown in
application page
505-c ("Exercise heart rate modal"). In other words, tapping on an aspect of
the application
page 505-b may cause the GUI 500 to display application page 505-c so that the
user may
quickly and easily view the heart rate of the user over time during a specific
activity, such as
the last exercise the user performed. The application page 505-c may include a
modal view
including details for the heart rate. In some cases, application page 505-c
may display an
exercise heart rate graph 510-c to represent how a user's rate changed during
an exercise,
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activity, etc. In some cases, a duration of the heart rate graph 510-c may be
based on an input
from the user. For example, the user may add a workout (e.g., using button
530), where the
user may indicate or the wearable device and/or application may otherwise
determine a
length of the workout, where the heart rate graph 510-c may be based on the
length.
Application page 505-c may also include an exercise heart rate range 525-a, a
maximum
exercise heart rate range 525-b, a minimum exercise heart rate 525-c, an
average exercise
heart rate 252-d, etc. The individual ranges may be on a per-workout basis,
for example.
[0140] In some implementations, the system 200 may be configured to
determine one or
more accuracy metrics for determined heart rate measurements (e.g., "signal
check" metrics).
Accuracy metrics may include any metrics or predictors of how accurate and/or
reliable a
determined heart rate measurement is for a user. In some aspects, accuracy
metrics may be
determined or derived from a strength/power of candidate heart rate
measurements, as well as
a quantity of strong candidate heart rate measurements within some measurement
period or
time interval. In cases of low signal quality (e.g., due to the ring being too
loose, motion
artifacts too intense, etc.), the system 200 may not be able to confidently
measure a user's
heart rate, which may result in relatively low accuracy metrics (e.g.,
relatively low
accuracy/reliability of heart rate measurements). In some implementations, the
system 200
may be configured to discard, ignore, or otherwise refrain from displaying
heart rate
measurements with accuracy metrics that are less than some threshold metric
(to avoid
presenting erroneous heart rate values to the user). In other words, the
system 200 may be
configured to display only heart rate measurements that exhibit some threshold
level of
accuracy/reliability (e.g., heart rate measurements with accuracy metrics that
satisfy some
threshold metric).
[0141] The server of system may cause the GUI 500 of the user device to
display a
message on application pages 505-a, 505-b, 505-c, or a combination thereof,
associated with
the identified heart rate data. The user device may display recommendations
and/or
information associated with the heart rate data via a message. In some
implementations, the
user device 106 and/or servers 110 may generate alerts (e.g., messages,
insights) associated
with the heart rate data, which may be displayed to the user via the GUI 500
(e.g., application
pages 505-a, 505-b, 50-c, or some other application page). In particular, the
messages
generated and displayed to the user via the GUI 500 may be associated with one
or more
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characteristics (e.g., time of day, duration, range) of the heart rate data.
For example, the
message may alert the user to breathe, take a moment to relax, etc., based on
the user's heart
rate. In some cases, the message may alert the user to perform an exercise or
activity because
it has been a duration greater than a threshold since the user last performed
an activity or
exercise. In some cases, the message may display a recommendation to the user
of how to
adjust their lifestyle to achieve a particular heart rate. For example, the
message may alert the
user to target a particular heart rate during exercise, where the
recommendation may be based
on a type of workout the user is performing. In some cases, the message may
alert the user to
reduce exercise intensity to lower the user's heart rate, or to increase
intensity to reach a
target heart rate, etc. In this regard, the system may be configured to
display messages or
insights to the user in order to facilitate effective, healthy patterns for
the user.
[0142] The heart rate graphs 510 may be illustrated via any visual
representation,
including line graphs, bar graphs, or any combination thereof For example, the
heart rate
graphs 510-a and 510-b may include line graphs, where the heart rate graph 510-
c may
include a line graph overlaid with (or on top of) heart rate measurements 535
(e.g., heart rate
measurements). In some implementations, the heart rate measurements 535, such
as heart rate
measurements 535-a and 535-b, may illustrate a range of potential heart rate
measurements
for a given period of time. In this regard, the relative height of the heart
rate measurements
may be associated with (or indicative of) a relative confidence metric for the
respective heart
rate measurement 535. For example, as shown in FIG. 5, the first heart rate
measurement
535-a exhibits a larger height as compared to the second heart rate
measurement 535-b,
which may indicate that the system 200 was able to calculate the second heart
rate
measurement 535-b with a higher confidence metric (e.g., more accurate
calculation) as
compared to the first heart rate measurement 535-a.
[0143] In some implementations, the user device 106 may be configured to
illustrate or
otherwise indicate heart rate measurements 535 associated with the heart rate
graphs 510 via
haptic feedback, audio feedback, and the like. In particular, the user device
106 may be
configured to provide haptic and/or audio feedback which corresponds to
different heart rate
measurements (e.g., heart rate measurements 535) as the user interacts with
(e.g., touches,
presses) different portions of the heart rate graphs 510.
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[0144] For example, a user may slide their finger (or a mouse, stylus,
etc.) along the heart
rate graph 510-c to select different heart rate measurements 535 within the
heart rate graph
510-c. In this example, the user device 106 may provide haptic feedback (or
audio feedback)
indicative of the selected heart rate measurements 535. For instance, in cases
where the first
heart rate measurement 535-a is 70 beats per minute, the user device 106 may
provide haptic
and/or audio feedback at a rate of 70 vibrations, beeps, chimes, etc., per
minute when the user
selects (e.g., presses and holds, clicks on, etc.) the first heart rate
measurement 535-a (or a
point on the heart rate graph 510-c corresponding to a point along the x-axis
corresponding
that is associated with the first heart rate measurement 535-a). Similarly, in
cases where the
second heart rate measurement 535-b is 85 beats per minute, the user device
106 may provide
haptic and/or audio feedback at a rate of 85 vibrations, beeps, chimes, etc.,
per minute when
the user selects (e.g., presses and holds, clicks on, etc.) the second heart
rate measurement
535-b (or a point on the heart rate graph 510-c corresponding to a point along
x-axis
corresponding to the second heart rate measurement 535-b).
[0145] FIG. 6 shows a block diagram 600 of a device 605 that supports
techniques for
measuring heart rate in accordance with aspects of the present disclosure. The
device 605
may include an input module 610, an output module 615, and a wearable
application 620.
The device 605 may also include a processor. Each of these components may be
in
communication with one another (e.g., via one or more buses).
[0146] The input module 610 may provide a means for receiving information
such as
packets, user data, control information, or any combination thereof associated
with various
information channels (e.g., control channels, data channels, information
channels related to
illness detection techniques). Information may be passed on to other
components of the
device 605. The input module 610 may utilize a single antenna or a set of
multiple antennas.
[0147] The output module 615 may provide a means for transmitting signals
generated by
other components of the device 605. For example, the output module 615 may
transmit
information such as packets, user data, control information, or any
combination thereof
associated with various information channels (e.g., control channels, data
channels,
information channels related to illness detection techniques). In some
examples, the output
module 615 may be co-located with the input module 610 in a transceiver
module. The output
module 615 may utilize a single antenna or a set of multiple antennas.
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[0148] For example, the wearable application 620 may include a
physiological data
component 625, a candidate heart rate determination component 630, a heart
rate selection
component 635, a heart rate determination component 640, or any combination
thereof In
some examples, the wearable application 620, or various components thereof,
may be
configured to perform various operations (e.g., receiving, monitoring,
transmitting) using or
otherwise in cooperation with the input module 610, the output module 615, or
both. For
example, the wearable application 620 may receive information from the input
module 610,
send information to the output module 615, or be integrated in combination
with the input
module 610, the output module 615, or both to receive information, transmit
information, or
perform various other operations as described herein.
[0149] The physiological data component 625 may be configured as or
otherwise support
a means for receiving physiological data associated with the user, the
physiological data
comprising PPG data and motion data collected throughout a first time interval
via a wearable
device associated with the user. The candidate heart rate determination
component 630 may
be configured as or otherwise support a means for determining a set of
candidate heart rate
measurements within the first time interval based at least in part on the PPG
data. The heart
rate selection component 635 may be configured as or otherwise support a means
for
selecting a first heart rate measurement from the set of candidate heart rate
measurements
based at least in part on the received motion data. The heart rate
determination component
640 may be configured as or otherwise support a means for determining a first
heart rate for
the user within the first time interval based at least in part on the selected
first heart rate
measurement.
[0150] FIG. 7 shows a block diagram 700 of a wearable application 720 that
supports
techniques for measuring heart rate in accordance with aspects of the present
disclosure. The
wearable application 720 may be an example of aspects of a wearable
application or a
wearable application 620, or both, as described herein. The wearable
application 720, or
various components thereof, may be an example of means for performing various
aspects of
techniques for measuring heart rate as described herein. For example, the
wearable
application 720 may include a physiological data component 725, a candidate
heart rate
determination component 730, a heart rate selection component 735, a heart
rate
determination component 740, a motion artifact identification component 745, a
PPG signal
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combination component 750, an acceleration data combination component 755, a
data display
component 760, a heart rate interpolation component 765, or any combination
thereof Each
of these components may communicate, directly or indirectly, with one another
(e.g., via one
or more buses).
[0151] The physiological data component 725 may be configured as or
otherwise support
a means for receiving physiological data associated with the user, the
physiological data
comprising PPG data and motion data collected throughout a first time interval
via a wearable
device associated with the user. The candidate heart rate determination
component 730 may
be configured as or otherwise support a means for determining a set of
candidate heart rate
measurements within the first time interval based at least in part on the PPG
data. The heart
rate selection component 735 may be configured as or otherwise support a means
for
selecting a first heart rate measurement from the set of candidate heart rate
measurements
based at least in part on the received motion data. The heart rate
determination component
740 may be configured as or otherwise support a means for determining a first
heart rate for
the user within the first time interval based at least in part on the selected
first heart rate
measurement.
[0152] In some examples, the motion artifact identification component 745
may be
configured as or otherwise support a means for identifying one or more heart
rate
measurements from the set of candidate heart rate measurements as motion
artifacts based at
least in part on a comparison of data trends between the set of candidate
heart rate
measurements and the motion data. In some examples, the heart rate selection
component 735
may be configured as or otherwise support a means for selecting the first
heart rate
measurement from a subset of the set of candidate heart rate measurements that
does not
include the one or more heart rate measurements which were identified as
motion artifacts.
[0153] In some examples, to support identifying the one or more heart rate
measurements
as motion artifacts, the motion artifact identification component 745 may be
configured as or
otherwise support a means for identifying the one or more heart rate
measurements from the
set of candidate heart rate measurements as motion artifacts based at least in
part on the one
or more heart rate measurements exhibiting a similar frequency pattern
relative to the motion
data.
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[0154] In some examples, the physiological data component 725 may be
configured as or
otherwise support a means for receiving additional physiological data
associated with the
user, the additional physiological data comprising additional PPG data and
additional motion
data collected throughout a second time interval via the wearable device, the
second time
interval subsequent to the first time interval. In some examples, the
candidate heart rate
determination component 730 may be configured as or otherwise support a means
for
determining an additional set of candidate heart rate measurements within the
second time
interval based at least in part on the additional PPG data. In some examples,
the heart rate
selection component 735 may be configured as or otherwise support a means for
selecting a
second heart rate measurement from the additional set of candidate heart rate
measurements
based at least in part on the received additional motion data. In some
examples, the heart rate
determination component 740 may be configured as or otherwise support a means
for
determining a second heart rate for the user within the second time interval
based at least in
part on the selected second heart rate measurement.
[0155] In some examples, the heart rate interpolation component 765 may be
configured
as or otherwise support a means for interpolating between the first heart rate
measurement for
the first time interval and the second heart rate measurement for the second
time interval,
wherein determining the first heart rate for the user within the first time
interval, determining
the second heart rate for the user within the second time interval, or both,
is based at least in
part on the interpolating.
[0156] In some examples, to support interpolating, the heart rate
interpolation component
765 may be configured as or otherwise support a means for interpolating
between the first
heart rate measurement for the first time interval and the second heart rate
measurement for
the second time interval based at least in part on an intensity of the motion
data collected
throughout the first time interval, an intensity of the additional motion data
collected
throughout the second time interval, or both.
[0157] In some examples, the PPG data comprises a plurality of PPG signals
acquired
from a plurality of pairs of PPG sensors, and the PPG signal combination
component 750
may be configured as or otherwise support a means for combining the plurality
of PPG
signals to generate a composite PPG signal using one or more mathematical
operations, the
one or more mathematical operations comprising an averaging operation, a
weighted
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averaging operation, or both, wherein determining the set of heart rate data
series is based at
least in part on the composite PPG signal.
[0158] In some examples, the motion data comprises first acceleration data
relative to a
first direction, and the acceleration data combination component 755 may be
configured as or
otherwise support a means for combining the first acceleration data, the
second acceleration
data, the third acceleration data, or any combination thereof, using one or
more mathematical
operations based at least in part on one or more characteristics of the first
acceleration data,
the second acceleration data, or the third acceleration data, wherein the
selection of the first
heart rate data series from the set of heart rate data series is based at
least in part on
combining the first acceleration data, the second acceleration data, the third
acceleration data,
or any combination thereof
[0159] In some examples, to support determining the set of candidate heart
rate
measurements, the candidate heart rate determination component 730 may be
configured as
or otherwise support a means for determining the set of candidate heart rate
measurements
within a frequency range that corresponds to an expected range of human heart
rates.
[0160] In some examples, the data display component 760 may be configured
as or
otherwise support a means for causing a GUI of a user device associated with
the user to
display an indication of the first heart rate.
[0161] In some examples, the wearable device comprises a wearable ring
device.
[0162] In some examples, the wearable device collects the physiological
data from the
user based on arterial blood flow.
[0163] FIG. 8 shows a diagram of a system 800 including a device 805 that
supports
techniques for measuring heart rate in accordance with aspects of the present
disclosure. The
device 805 may be an example of or include the components of a device 605 as
described
herein. The device 805 may include an example of a user device 106, as
described previously
herein. The device 805 may include components for bi-directional
communications including
components for transmitting and receiving communications with a wearable
device 104 and a
server 110, such as a wearable application 820, a communication module 810, an
antenna
815, a user interface component 825, a database (application data) 830, a
memory 835, and a
processor 840. These components may be in electronic communication or
otherwise coupled
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(e.g., operatively, communicatively, functionally, electronically,
electrically) via one or more
buses (e.g., a bus 845).
[0164] The communication module 810 may manage input and output signals for
the
device 805 via the antenna 815. The communication module 810 may include an
example of
the communication module 220-b of the user device 106 shown and described in
FIG. 2. In
this regard, the communication module 810 may manage communications with the
ring 104
and the server 110, as illustrated in FIG. 2. The communication module 810 may
also manage
peripherals not integrated into the device 805. In some cases, the
communication module 810
may represent a physical connection or port to an external peripheral. In some
cases, the
communication module 810 may utilize an operating system such as i0S0, ANDROID
,
MS-DOS , MS-WINDOWSO, OS/20, UNIX , LINUX , or another known operating
system. In other cases, the communication module 810 may represent or interact
with a
wearable device (e.g., ring 104), modem, a keyboard, a mouse, a touchscreen,
or a similar
device. In some cases, the communication module 810 may be implemented as part
of the
processor 840. In some examples, a user may interact with the device 805 via
the
communication module 810, user interface component 825, or via hardware
components
controlled by the communication module 810.
[0165] In some cases, the device 805 may include a single antenna 815.
However, in
some other cases, the device 805 may have more than one antenna 815, which may
be
capable of concurrently transmitting or receiving multiple wireless
transmissions. The
communication module 810 may communicate bi-directionally, via the one or more
antennas
815, wired, or wireless links as described herein. For example, the
communication module
810 may represent a wireless transceiver and may communicate bi-directionally
with another
wireless transceiver. The communication module 810 may also include a modem to
modulate
the packets, to provide the modulated packets to one or more antennas 815 for
transmission,
and to demodulate packets received from the one or more antennas 815.
[0166] The user interface component 825 may manage data storage and
processing in a
database 830. In some cases, a user may interact with the user interface
component 825. In
other cases, the user interface component 825 may operate automatically
without user
interaction. The database 830 may be an example of a single database, a
distributed database,
multiple distributed databases, a data store, a data lake, or an emergency
backup database.
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[0167] The memory 835 may include RAM and ROM. The memory 835 may store
computer-readable, computer-executable software including instructions that,
when executed,
cause the processor 840 to perform various functions described herein. In some
cases, the
memory 835 may contain, among other things, a basic I/O system (BIOS) which
may control
basic hardware or software operation such as the interaction with peripheral
components or
devices.
[0168] The processor 840 may include an intelligent hardware device, (e.g.,
a general-
purpose processor, a digital signal processor (DSP), a central processing unit
(CPU), a
microcontroller, an application-specific integrated circuit (ASIC), an field-
programmable
gate array (FPGA), a programmable logic device, a discrete gate or transistor
logic
component, a discrete hardware component, or any combination thereof). In some
cases, the
processor 840 may be configured to operate a memory array using a memory
controller. In
other cases, a memory controller may be integrated into the processor 840. The
processor 840
may be configured to execute computer-readable instructions stored in a memory
835 to
perform various functions (e.g., functions or tasks supporting a method and
system for sleep
staging algorithms).
[0169] For example, the wearable application 820 may be configured as or
otherwise
support a means for receiving physiological data associated with the user, the
physiological
data comprising PPG data and motion data collected throughout a first time
interval via a
wearable device associated with the user. The wearable application 820 may be
configured as
or otherwise support a means for determining a set of candidate heart rate
measurements
within the first time interval based at least in part on the PPG data. The
wearable application
820 may be configured as or otherwise support a means for selecting a first
heart rate
measurement from the set of candidate heart rate measurements based at least
in part on the
received motion data. The wearable application 820 may be configured as or
otherwise
support a means for determining a first heart rate for the user within the
first time interval
based at least in part on the selected first heart rate measurement.
[0170] By including or configuring the wearable application 820 in
accordance with
examples as described herein, the device 805 may support techniques for
improved heart rate
data for a user, and improved alerts or instructions provided to a user.
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[0171] The wearable application 820 may include an application (e.g.,
"app"), program,
software, or other component that is configured to facilitate communications
with a ring 104,
server 110, other user devices 106, and the like. For example, the wearable
application 820
may include an application executable on a user device 106 that is configured
to receive data
(e.g., physiological data) from a ring 104, perform processing operations on
the received data,
transmit and receive data with the servers 110, and cause presentation of data
to a user 102.
[0172] FIG. 9 shows a flowchart illustrating a method 900 that supports
techniques for
measuring heart rate in accordance with aspects of the present disclosure. The
operations of
the method 900 may be implemented by a user device or its components as
described herein.
For example, the operations of the method 900 may be performed by a user
device as
described with reference to FIGs. 1 through 8. In some examples, a user device
may execute
a set of instructions to control the functional elements of the user device to
perform the
described functions. Additionally, or alternatively, the user device may
perform aspects of the
described functions using special-purpose hardware.
[0173] At 905, the method may include receiving physiological data
associated with the
user, the physiological data comprising PPG data and motion data collected
throughout a first
time interval via a wearable device associated with the user. The operations
of 905 may be
performed in accordance with examples as disclosed herein. In some examples,
aspects of the
operations of 905 may be performed by a physiological data component 725 as
described
with reference to FIG. 7.
[0174] At 910, the method may include determining a set of candidate heart
rate
measurements within the first time interval based at least in part on the PPG
data. The
operations of 910 may be performed in accordance with examples as disclosed
herein. In
some examples, aspects of the operations of 910 may be performed by a
candidate heart rate
determination component 730 as described with reference to FIG. 7.
[0175] At 915, the method may include selecting a first heart rate
measurement from the
set of candidate heart rate measurements based at least in part on the
received motion data.
The operations of 915 may be performed in accordance with examples as
disclosed herein. In
some examples, aspects of the operations of 915 may be performed by a heart
rate selection
component 735 as described with reference to FIG. 7.
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[0176] At 920, the method may include determining a first heart rate for
the user within
the first time interval based at least in part on the selected first heart
rate measurement. The
operations of 920 may be performed in accordance with examples as disclosed
herein. In
some examples, aspects of the operations of 920 may be performed by a heart
rate
determination component 740 as described with reference to FIG. 7.
[0177] FIG. 10 shows a flowchart illustrating a method 1000 that supports
techniques for
measuring heart rate in accordance with aspects of the present disclosure. The
operations of
the method 1000 may be implemented by a user device or its components as
described herein.
For example, the operations of the method 1000 may be performed by a user
device as
described with reference to FIGs. 1 through 8. In some examples, a user device
may execute
a set of instructions to control the functional elements of the user device to
perform the
described functions. Additionally, or alternatively, the user device may
perform aspects of the
described functions using special-purpose hardware.
[0178] At 1005, the method may include receiving physiological data
associated with the
user, the physiological data comprising PPG data and motion data collected
throughout a first
time interval via a wearable device associated with the user. The operations
of 1005 may be
performed in accordance with examples as disclosed herein. In some examples,
aspects of the
operations of 1005 may be performed by a physiological data component 725 as
described
with reference to FIG. 7.
[0179] At 1010, the method may include combining the plurality of PPG
signals to
generate a composite PPG signal using one or more mathematical operations, the
one or more
mathematical operations comprising an averaging operation, a weighted
averaging operation,
or both. The operations of 1010 may be performed in accordance with examples
as disclosed
herein. In some examples, aspects of the operations of 1010 may be performed
by a PPG
signal combination component 750 as described with reference to FIG. 7.
[0180] At 1015, the method may include determining a set of candidate heart
rate
measurements within the first time interval based at least in part on the PPG
data, wherein
determining the set of candidate heart rate measurements is based at least in
part on the
composite PPG signal. The operations of 1015 may be performed in accordance
with
examples as disclosed herein. In some examples, aspects of the operations of
1015 may be
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performed by a candidate heart rate determination component 730 as described
with reference
to FIG. 7.
[0181] At 1020, the method may include selecting a first heart rate
measurement from the
set of candidate heart rate measurements based at least in part on the
received motion data.
The operations of 1020 may be performed in accordance with examples as
disclosed herein.
In some examples, aspects of the operations of 1020 may be performed by a
heart rate
selection component 735 as described with reference to FIG. 7.
[0182] At 1025, the method may include determining a first heart rate for
the user within
the first time interval based at least in part on the selected first heart
rate measurement. The
operations of 1025 may be performed in accordance with examples as disclosed
herein. In
some examples, aspects of the operations of 1025 may be performed by a heart
rate
determination component 740 as described with reference to FIG. 7.
[0183] FIG. 11 shows a flowchart illustrating a method 1100 that supports
techniques for
measuring heart rate in accordance with aspects of the present disclosure. The
operations of
the method 1100 may be implemented by a user device or its components as
described herein.
For example, the operations of the method 1100 may be performed by a user
device as
described with reference to FIGs. 1 through 8. In some examples, a user device
may execute
a set of instructions to control the functional elements of the user device to
perform the
described functions. Additionally, or alternatively, the user device may
perform aspects of the
described functions using special-purpose hardware.
[0184] At 1105, the method may include receiving physiological data
associated with the
user, the physiological data comprising PPG data and motion data collected
throughout a first
time interval via a wearable device associated with the user. The operations
of 1105 may be
performed in accordance with examples as disclosed herein. In some examples,
aspects of the
operations of 1105 may be performed by a physiological data component 725 as
described
with reference to FIG. 7.
[0185] At 1110, the method may include determining a set of candidate heart
rate
measurements within the first time interval based at least in part on the PPG
data. The
operations of 1110 may be performed in accordance with examples as disclosed
herein. In
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some examples, aspects of the operations of 1110 may be performed by a
candidate heart rate
determination component 730 as described with reference to FIG. 7.
[0186] At 1115, the method may include selecting a first heart rate
measurement from the
set of candidate heart rate measurements based at least in part on the
received motion data.
The operations of 1115 may be performed in accordance with examples as
disclosed herein.
In some examples, aspects of the operations of 1115 may be performed by a
heart rate
selection component 735 as described with reference to FIG. 7.
[0187] At 1120, the method may include determining a first heart rate for
the user within
the first time interval based at least in part on the selected first heart
rate measurement. The
operations of 1120 may be performed in accordance with examples as disclosed
herein. In
some examples, aspects of the operations of 1120 may be performed by a heart
rate
determination component 740 as described with reference to FIG. 7.
[0188] At 1125, the method may include causing a GUI of a user device
associated with
the user to display an indication of the first heart rate. The operations of
1125 may be
performed in accordance with examples as disclosed herein. In some examples,
aspects of the
operations of 1125 may be performed by a data display component 760 as
described with
reference to FIG. 7.
[0189] It should be noted that the methods described above describe
possible
implementations, and that the operations and the steps may be rearranged or
otherwise
modified and that other implementations are possible. Furthermore, aspects
from two or more
of the methods may be combined.
[0190] A method is described. The method may include receiving
physiological data
associated with the user, the physiological data comprising PPG data and
motion data
collected throughout a first time interval via a wearable device associated
with the user,
determining a set of candidate heart rate measurements within the first time
interval based at
least in part on the PPG data, selecting a first heart rate measurement from
the set of
candidate heart rate measurements based at least in part on the received
motion data, and
determining a first heart rate for the user within the first time interval
based at least in part on
the selected first heart rate measurement.
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[0191] An apparatus is described. The apparatus may include a processor,
memory
coupled with the processor, and instructions stored in the memory. The
instructions may be
executable by the processor to cause the apparatus to receive physiological
data associated
with the user, the physiological data comprising PPG data and motion data
collected
throughout a first time interval via a wearable device associated with the
user, determine a set
of candidate heart rate measurements within the first time interval based at
least in part on the
PPG data, select a first heart rate measurement from the set of candidate
heart rate
measurements based at least in part on the received motion data, and determine
a first heart
rate for the user within the first time interval based at least in part on the
selected first heart
rate measurement.
[0192] Another apparatus is described. The apparatus may include means for
receiving
physiological data associated with the user, the physiological data comprising
PPG data and
motion data collected throughout a first time interval via a wearable device
associated with
the user, means for determining a set of candidate heart rate measurements
within the first
time interval based at least in part on the PPG data, means for selecting a
first heart rate
measurement from the set of candidate heart rate measurements based at least
in part on the
received motion data, and means for determining a first heart rate for the
user within the first
time interval based at least in part on the selected first heart rate
measurement.
[0193] A non-transitory computer-readable medium storing code is described.
The code
may include instructions executable by a processor to receive physiological
data associated
with the user, the physiological data comprising PPG data and motion data
collected
throughout a first time interval via a wearable device associated with the
user, determine a set
of candidate heart rate measurements within the first time interval based at
least in part on the
PPG data, select a first heart rate measurement from the set of candidate
heart rate
measurements based at least in part on the received motion data, and determine
a first heart
rate for the user within the first time interval based at least in part on the
selected first heart
rate measurement.
[0194] Some examples of the method, apparatuses, and non-transitory
computer-readable
medium described herein may further include operations, features, means, or
instructions for
identifying one or more heart rate measurements from the set of candidate
heart rate
measurements as motion artifacts based at least in part on a comparison of
data trends
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between the set of candidate heart rate measurements and the motion data and
selecting the
first heart rate measurement from a subset of the set of candidate heart rate
measurements
that does not include the one or more heart rate measurements which were
identified as
motion artifacts.
[0195] In some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein, identifying the one or more heart rate
measurements as
motion artifacts may include operations, features, means, or instructions for
identifying the
one or more heart rate measurements from the set of candidate heart rate
measurements as
motion artifacts based at least in part on the one or more heart rate
measurements exhibiting a
similar frequency pattern relative to the motion data.
[0196] Some examples of the method, apparatuses, and non-transitory
computer-readable
medium described herein may further include operations, features, means, or
instructions for
receiving additional physiological data associated with the user, the
additional physiological
data comprising additional PPG data and additional motion data collected
throughout a
second time interval via the wearable device, the second time interval
subsequent to the first
time interval, determining an additional set of candidate heart rate
measurements within the
second time interval based at least in part on the additional PPG data,
selecting a second heart
rate measurement from the additional set of candidate heart rate measurements
based at least
in part on the received additional motion data, and determining a second heart
rate for the
user within the second time interval based at least in part on the selected
second heart rate
measurement.
[0197] Some examples of the method, apparatuses, and non-transitory
computer-readable
medium described herein may further include operations, features, means, or
instructions for
interpolating between the first heart rate measurement for the first time
interval and the
second heart rate measurement for the second time interval, wherein
determining the first
heart rate for the user within the first time interval, determining the second
heart rate for the
user within the second time interval, or both, may be based at least in part
on the
interpolating.
[0198] In some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein, the interpolating may include operations,
features, means,
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or instructions for interpolating between the first heart rate measurement for
the first time
interval and the second heart rate measurement for the second time interval
based at least in
part on an intensity of the motion data collected throughout the first time
interval, an intensity
of the additional motion data collected throughout the second time interval,
or both.
[0199] In some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein, the PPG data comprises a plurality of PPG
signals
acquired from a plurality of pairs of PPG sensors and the method, apparatuses,
and non-
transitory computer-readable medium may include further operations, features,
means, or
instructions for combining the plurality of PPG signals to generate a
composite PPG signal
using one or more mathematical operations, the one or more mathematical
operations
comprising an averaging operation, a weighted averaging operation, or both,
wherein
determining the set of heart rate data series may be based at least in part on
the composite
PPG signal.
[0200] In some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein, the motion data comprises first acceleration
data relative
to a first direction and the method, apparatuses, and non-transitory computer-
readable
medium may include further operations, features, means, or instructions for
combining the
first acceleration data, the second acceleration data, the third acceleration
data, or any
combination thereof, using one or more mathematical operations based at least
in part on one
or more characteristics of the first acceleration data, the second
acceleration data, or the third
acceleration data, wherein the selection of the first heart rate data series
from the set of heart
rate data series may be based at least in part on combining the first
acceleration data, the
second acceleration data, the third acceleration data, or any combination
thereof
[0201] In some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein, determining the set of candidate heart rate
measurements
may include operations, features, means, or instructions for determining the
set of candidate
heart rate measurements within a frequency range that corresponds to an
expected range of
human heart rates.
[0202] Some examples of the method, apparatuses, and non-transitory
computer-readable
medium described herein may further include operations, features, means, or
instructions for
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causing a GUI of a user device associated with the user to display an
indication of the first
heart rate.
[0203] In some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein, the wearable device comprises a wearable
ring device.
[0204] In some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein, the wearable device collects the
physiological data from
the user based on arterial blood flow.
[0205] The description set forth herein, in connection with the appended
drawings,
describes example configurations and does not represent all the examples that
may be
implemented or that are within the scope of the claims. The term "exemplary"
used herein
means "serving as an example, instance, or illustration," and not "preferred"
or
"advantageous over other examples." The detailed description includes specific
details for the
purpose of providing an understanding of the described techniques. These
techniques,
however, may be practiced without these specific details. In some instances,
well-known
structures and devices are shown in block diagram form in order to avoid
obscuring the
concepts of the described examples.
[0206] In the appended figures, similar components or features may have the
same
reference label. Further, various components of the same type may be
distinguished by
following the reference label by a dash and a second label that distinguishes
among the
similar components. If just the first reference label is used in the
specification, the description
is applicable to any one of the similar components having the same first
reference label
irrespective of the second reference label.
[0207] Information and signals described herein may be represented using
any of a
variety of different technologies and techniques. For example, data,
instructions, commands,
information, signals, bits, symbols, and chips that may be referenced
throughout the above
description may be represented by voltages, currents, electromagnetic waves,
magnetic fields
or particles, optical fields or particles, or any combination thereof
[0208] The various illustrative blocks and modules described in connection
with the
disclosure herein may be implemented or performed with a general-purpose
processor, a
DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or
transistor
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logic, discrete hardware components, or any combination thereof designed to
perform the
functions described herein. A general-purpose processor may be a
microprocessor, but in the
alternative, the processor may be any conventional processor, controller,
microcontroller, or
state machine. A processor may also be implemented as a combination of
computing devices
(e.g., a combination of a DSP and a microprocessor, multiple microprocessors,
one or more
microprocessors in conjunction with a DSP core, or any other such
configuration).
[0209] The functions described herein may be implemented in hardware,
software
executed by a processor, firmware, or any combination thereof If implemented
in software
executed by a processor, the functions may be stored on or transmitted over as
one or more
instructions or code on a computer-readable medium. Other examples and
implementations
are within the scope of the disclosure and appended claims. For example, due
to the nature of
software, functions described above can be implemented using software executed
by a
processor, hardware, firmware, hardwiring, or combinations of any of these.
Features
implementing functions may also be physically located at various positions,
including being
distributed such that portions of functions are implemented at different
physical locations.
Also, as used herein, including in the claims, "or" as used in a list of items
(for example, a list
of items prefaced by a phrase such as "at least one of' or "one or more of')
indicates an
inclusive list such that, for example, a list of at least one of A, B, or C
means A or B or C or
AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein, the phrase
"based on"
shall not be construed as a reference to a closed set of conditions. For
example, an exemplary
step that is described as "based on condition A" may be based on both a
condition A and a
condition B without departing from the scope of the present disclosure. In
other words, as
used herein, the phrase "based on" shall be construed in the same manner as
the phrase
"based at least in part on."
[0210] Computer-readable media includes both non-transitory computer
storage media
and communication media including any medium that facilitates transfer of a
computer
program from one place to another. A non-transitory storage medium may be any
available
medium that can be accessed by a general purpose or special purpose computer.
By way of
example, and not limitation, non-transitory computer-readable media can
comprise RAM,
ROM, electrically erasable programmable ROM (EEPROM), compact disk (CD) ROM or
other optical disk storage, magnetic disk storage or other magnetic storage
devices, or any
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other non-transitory medium that can be used to carry or store desired program
code means in
the form of instructions or data structures and that can be accessed by a
general-purpose or
special-purpose computer, or a general-purpose or special-purpose processor.
Also, any
connection is properly termed a computer-readable medium. For example, if the
software is
transmitted from a website, server, or other remote source using a coaxial
cable, fiber optic
cable, twisted pair, digital subscriber line (DSL), or wireless technologies
such as infrared,
radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair,
DSL, or wireless
technologies such as infrared, radio, and microwave are included in the
definition of medium.
Disk and disc, as used herein, include CD, laser disc, optical disc, digital
versatile disc
(DVD), floppy disk and Blu-ray disc where disks usually reproduce data
magnetically, while
discs reproduce data optically with lasers. Combinations of the above are also
included within
the scope of computer-readable media.
[0211] The description herein is provided to enable a person skilled in the
art to make or
use the disclosure. Various modifications to the disclosure will be readily
apparent to those
skilled in the art, and the generic principles defined herein may be applied
to other variations
without departing from the scope of the disclosure. Thus, the disclosure is
not limited to the
examples and designs described herein, but is to be accorded the broadest
scope consistent
with the principles and novel features disclosed herein.
