Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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ANONYMIZED HEALTH MONITORING PLATFORM
CROSS REFERENCE
[0001] The present Application for Patent claims the benefit of U.S.
Patent
Application No. 17/807,010 by GILAN et al., entitled "ANONYMIZED HEALTH
MONITORING PLATFORM," filed June 15, 2022 and U.S. Provisional Patent
Application No. 63/211,506 by GILAN et al., entitled "ANONYMIZED HEALTH
MONITORING PLATFORM," filed June 16, 2021; each of which is assigned to the
assignee thereof, and each of which is expressly incorporated by reference
herein.
FIELD OF TECHNOLOGY
[0002] The following relates to wearable devices and data processing,
including an
anonymized health monitoring platform.
BACKGROUND
[0003] Some wearable devices may be configured to collect physiological
data from
users, including temperature data, heart rate data, and the like. Many users
have a desire
for more insight regarding their physical health.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIGs. 1 and 2 illustrate examples of systems that support an
anonymized
health monitoring platform in accordance with aspects of the present
disclosure.
[0005] FIG. 3 illustrates an example of a health monitoring platform that
supports
an anonymized health monitoring platform in accordance with aspects of the
present
disclosure.
[0006] FIGs. 4-7 illustrate examples of graphical user interfaces (GUIs)
that
support anonymized health monitoring platform in accordance with aspects of
the
present disclosure.
[0007] FIG. 8 shows a block diagram of an apparatus that supports an
anonymized
health monitoring platform in accordance with aspects of the present
disclosure.
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[0008] FIG. 9 shows a block diagram of a wearable application that
supports an
anonymized health monitoring platform in accordance with aspects of the
present
disclosure.
[0009] FIG. 10 shows a diagram of a system including a device that
supports an
anonymized health monitoring platform in accordance with aspects of the
present
disclosure.
[0010] FIGs. 11 through 13 show flowcharts illustrating methods that
support
anonymized health monitoring platform in accordance with aspects of the
present
disclosure.
DETAILED DESCRIPTION
[0011] Some wearable devices may be configured to collect data from users
associated with movement and other activities. For example, some wearable
devices
may be configured to continuously acquire physiological data associated with a
user
including temperature data, heart rate data, and the like. In order to
efficiently and
accurately track physiological data, a wearable device may be configured to
collect data
continuously while the user wears the device.
[0012] Most individuals do not have the capability to continually monitor
their
health. For example, most individuals go to the doctor a few times a year. As
such, most
individuals have several snapshots of their health-related data (e.g.,
temperature, heart
rate, blood pressure, etc.) at a few points in time throughout the year. The
limited data
points, along with the infrequent and inconsistent times that the data points
are
collected, provide a limited view of the user's overall health. Moreover,
these data
points may often be taken after an individual knows they are sick, which
further limits
the utility of the collected data points. As such, techniques for health
monitoring with
infrequent snapshots of health-related data may be deficient for multiple
reasons.
[0013] Accordingly, to facilitate improved health monitoring, aspects of
the present
disclosure are directed to a health monitoring platform configured to
continuously
collect physiological data from users to provide continuous health monitoring.
Specifically, aspects of the present disclosure are directed to techniques for
calculating
health risk metrics (e.g., illness risk metrics) for users based on acquired
data, and
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alerting users and/or administrators (e.g., doctors, coaches, managers,
employers) of a
potential health risk when a user exhibits a health risk metric that satisfies
(e.g.,
exceeds) one or more predefined thresholds.
[0014] In some implementations, wearable devices may be used to
continuously
acquire physiological data and other data (e.g., sleep data) from a group of
users for
improved health monitoring. The respective users and/or an administrator of
the group
of users may have access to the acquired physiological data and/or scores and
metrics
that are determined based on the acquired physiological data (e.g., health
risk metrics,
Sleep Scores, Readiness Scores). In some implementations, users may be able to
view
their physiological data and/or calculated scores/metrics. Comparatively, in
some cases,
physiological data and/or calculated scores/metrics may be presented to
administrators
in an anonymized manner to improve the privacy of the respective users.
[0015] For example, an administrator associated with a group of users may
access a
user interface that displays the physiological data and/or scores/metrics
(e.g., health risk
metrics, Sleep Scores, Readiness Scores) for each user of the group. In some
examples,
the administrator may know the identity of each user. In some other examples,
each of
the physiological data and/or calculated metrics/scores for each user may be
anonymized. For instance, physiological data and health risk scores for each
user may
be presented to the administrator along with an anonymized user identifier
associated
with each respective user. By anonymously presenting acquired physiological
data and
calculated scores/metrics, the privacy and security for each of the respective
users may
be preserved.
[0016] In some implementations, an administrator of a group of users may
set or
otherwise define thresholds for each metric or score, where the predefined
thresholds
may trigger messaging or other actions when the respective thresholds are
satisfied. For
example, if the health monitoring system determines that a health risk metric
for a user
satisfies (e.g., exceeds) a predefined threshold, thereby indicating a
presence of a
potential health risk, the system may automatically trigger transmission of a
message to
the user and/or an administrator. In cases with non-anonymized health
monitoring, the
health monitoring platform may tell the administrator which user is associated
with the
potential health risk so that the administrator may directly contact the user.
Conversely,
in cases with anonymized health monitoring, the administrator may not know the
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identity of the user associated with the potential health risk. However, in
such cases, the
administrator may see whether the user has viewed a message triggered by the
identified
health risk, and may decide to resend the message to the user after some time
duration.
[0017] While much of the present disclosure is described in the context
of health
risk metrics related to illness, this is not to be regarded as a limitation of
the present
disclosure. Indeed, it is contemplated herein that aspects of the present
disclosure may
be used for continuously monitoring user health with respect to any potential
health
condition, including potential cardiac conditions/episodes, insomnia, atrial
fibrillation,
and the like. In particular, techniques described herein may enable the
detection of
illness prior to a user actually experiencing symptoms (e.g., pre-symptomatic
illness
detection), which may help reduce the spread of illness. Moreover,
physiological data
associated with a user may be used to update any score, measure, metric, or
other
abstraction associated with a user's health or activity. The health monitoring
system
may detect multiple health conditions, and may display information related to
health
metrics for each illness.
[0018] 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 described in the context of example graphical
user
interfaces (GUIs). Aspects of the disclosure are further illustrated by and
described with
reference to apparatus diagrams, system diagrams, and flowcharts that relate
to an
anonymized health monitoring platform.
[0019] FIG. 1 illustrates an example of a system 100 that supports an
anonymized
health monitoring platform 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.
[0020] 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)
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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.
[0021] 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. 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. 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.
[0022] 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).
[0023] 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,
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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,
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.
[0024] 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 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.
[0025] 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.
[0026] 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
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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.
[0027] 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 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.
[0028] 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.
[0029] In some aspects, the respective devices of the system 100 may
support
techniques for monitoring health metrics related to illness by using a
wearable device.
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In particular, the system 100 illustrated in FIG. 1 may support techniques for
using a
risk score for assessing illness likelihood of a user 102, and notifying the
user 102 if the
risk score surpasses an acceptable threshold. For example, as shown in FIG. 1,
User 1
(e.g., user 102-a) may be associated with a wearable device (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, including temperature, heart rate, HRV, and the like. In some
aspects,
data collected by the ring 104-a may be used to determine whether User 1 is
ill, or will
become ill. For example, the data may be used to calculate a health risk
metric
indicating a relative probability that User 1 is ill, or will become ill
(e.g., relative
probability that User 1 will transition from a healthy state to an unhealthy
state).
[0030] Detection of illness and calculation of a health risk metrics 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 The system 100 may be configured to perform pre-
symptomatic
illness detection, in which illness is identified or predicted prior to a user
102
experiencing symptoms (e.g., prior to symptom onset). The system 100 may
detect one
or more illnesses for the user 102. In some cases, calculated health risk
metrics may be
compared to predefined thresholds to perform the illness detection/health
monitoring
techniques described herein. In some cases, the predefined thresholds may be
set or
adjusted by an administrator based on a relative acceptable health risk metric
for each
user 102 in the system (e.g., User 1, User 2 . . . User N).
[0031] Upon detecting that a health risk metric for a user 102 satisfies
a predefined
threshold, the system 100 may notify the user 102 and/or a system
administrator of one
or more potential health risks. For example, upon detecting the health risk
metric for a
User 1 satisfies a predefined threshold, the system 100 may display a
notification of the
potential health risk via the user device 106-a. Moreover, the system 100 may
monitor
whether the User 1 viewed the notification of the potential health risk (e.g.,
via a GUI of
the mobile device), and may selectively send a reminder accordingly (e.g.,
transmit a
reminder or follow-up notification if the User 1 has not viewed the
notification). In
some implementations, the notifications of potential health risks associated
with User 1
may be sent to User 1 (e.g., user device 106-a), an administrative personnel
(e.g.,
manager of the group of users 102-a, 102-b, 102-c), or both. In some
implementations,
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the administrator may know the identity of User 1, and may transmit the
notification of
the detected potential health risk to User 1 directly. In some other cases,
health
monitoring may be performed and reported to administrators anonymously such
that the
administrators may be able to view an anonymous list of users and
corresponding health
risk metrics. In such cases, the system 100 may notify User 1 and the
administrator of
the potential illness, where the identity of the user remains anonymous to the
administrator. That is, administrator may view health risk metrics and/or
physiological
data for a group of users, but may not know what health risk metrics and/or
physiological data corresponds to which user.
[0032] In some implementations, an administrator may view that one or
more users
102 exhibit health risk metrics above a predefined threshold, and may view
whether the
users 102 have seen any notifications related to one or more potential health
risks (e.g.,
one or more potential illnesses). In some implementations, the system 100 may
generate
the notification of potential health risks for User 1 (e.g., via the ring 104,
user device
106, or both) based on the detected health risk metric, where the alerts
indicate to the
user 102 the potential health risks. The generated alerts may additionally or
alternatively, provide other insights regarding the potential health risks,
such as health
trends for User 1, contributing factors for the detected health risks, whether
User 1
should consider contacting a medical professional, whether the user should
stay home
from work or other activities, and the like.
[0033] Techniques described herein may support a health monitoring
platform that
enables anonymized health tracking for a group of users. In particular,
techniques
described herein may enable physiological data for a group of users to be
continuously
tracked in order to identify users who are likely to be sick, or likely to
become sick in
the near future. By notifying users and/or administrators (e.g., doctors,
coaches, office
managers, business owners) of potential illness, techniques described herein
may reduce
a spread of illness, and improve health tracking. Moreover, by providing
notifications of
health risk metrics in an anonymous manner, techniques described herein may
enable
administrators to track a risk of illness throughout a group of users while
simultaneously
maintaining a level of privacy with respect to physiological data acquired
from the
respective users.
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[0034] 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 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.
[0035] FIG. 2 illustrates an example of a system 200 that supports an
anonymized
health monitoring platform 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.
[0036] 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.
[0037] 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,
photoplethysmogram
(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.
[0038] 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
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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.
[0039] 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.
[0040] 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., 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.
[0041] 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.,
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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.
[0042] 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.
[0043] 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-a. 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.
[0044] 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.
[0045] 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
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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).
[0046] 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 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.).
[0047] 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.
[0048] 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.
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[0049] 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 components. Accordingly, the modules may also be referred
to as
circuits (e.g., a communication circuit and power circuit).
[0050] 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.
[0051] 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.
[0052] 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
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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, 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.
[0053] 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.
[0054] 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
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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.
[0055] 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 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.
[0056] 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.
[0057] 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
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temperatures (e.g., one per minute) instead of sampled temperatures in order
to conserve
memory 215.
[0058] 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 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).
[0059] 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.
[0060] 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.
[0061] 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.
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[0062] 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 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.
[0063] 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.
[0064] 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
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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).
[0065] 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 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.
[0066] 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.
[0067] 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).
[0068] 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
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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.
[0069] 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.
[0070] 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.
[0071] 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
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(MEMS) sensor that may measure angular rates and accelerations in three
perpendicular
axes.
[0072] 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).
[0073] 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 (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.
[0074] 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.
[0075] In some implementations, motion counts and regularity values may
be
determined by counting a number of acceleration peaks within one or more
periods of
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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.
[0076] 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 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.
[0077] 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.
[0078] 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,
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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.
[0079] 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 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.
[0080] 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.
[0081] 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
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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.
[0082] 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. 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.
[0083] 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, rapid eye movement (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
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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).
[0084] 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. Lastly, the "timing" contributor may 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.
[0085] 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.
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[0086] 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.
[0087] 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 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.
[0088] In some aspects, the ring 104, user device 106, and servers 110 of
the system
200 may be configured to evaluate physiological data and sleep patterns for a
user 102.
In particular, the respective components of the system 200 may be used to
determine/predict potential illness for the user based on the acquired
physiological data
and sleep patterns for the user. In particular, the system 200 may be
configured to
detect/predict illness onset within users 102 prior to the respective users
102
experiencing any noticeable symptoms of illness. This pre-symptomatic illness
detection may reduce the severity of illness, and reduce the spread of illness
within a
population.
[0089] For example, as noted previously herein, the ring 104 of the
system 200 may
be worn by a user to collect physiological data from the user, including
temperature
data, heart rate data, HRV data, and the like. Data collected by the ring 104
may be used
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to determine scores (e.g., Sleep Scores, Readiness Scores) for the user for
each sleep
day. Additionally or alternatively, data collected by the ring 104 may be used
to
determine one or more health risk metrics for the user related to potential
illness and/or
other health concerns. For example, the system 200 may determine an illness
risk metric
(e.g., risk score) for a given day (e.g., which may be a 24 hour period of
continuous
monitoring), where the illness risk metric is associated with a relative
probability that
the user will become ill with any number of illnesses or other health concerns
(e.g.,
transition from a healthy state to an unhealthy state). In some aspects,
health risk
metrics may be calculated for the user for each respective day, such that a
first day is
associated with a first set of health risk metrics, and a second day is
associated with a
second set of health risk metrics. Health risk metrics may be calculated for
each day
based on data collected by the ring 104 during the respective day. Health risk
metrics
may include, but are not limited to, risk scores, health scores, illness risk
metrics, and
the like.
[0090] FIG. 3 illustrates an example of a health monitoring platform 300
that
supports an anonymized health monitoring platform in accordance with aspects
of the
present disclosure. The health monitoring platform 300 may implement, or be
implemented by, aspects of the system 100, system 200, or both. In particular,
health
monitoring platform 300 illustrates an example of one or more wearable devices
104,
such as rings, and user devices 106, as described with reference to FIGs. 1
and 2. In
some examples, health monitoring platform 300 may represent a health
monitoring
platform for detecting one or more illnesses in one or more users 102.
[0091] In some examples, the health monitoring platform 300 may collect
data from
one or more users 102 via a wearable device 104, such as a ring. Each wearable
device
104 may communicate with one or more user devices 106, such as a computer,
cellular
device, tablet, another wearable device 104, or other user device 106 of a
user 102. For
example, wearable device 104-a, which may include a ring, may acquire
physiological
data from the user 102-a and transmit the acquired physiological data to the
user device
106-a and/or server 110 for processing. The user device 106-a may then display
the
processed physiological data and/or other determined parameters/metrics to the
user
102-a. Similarly, wearable device 104-b may send data to user device 106-b for
display
to user 102-b. Wearable device 104-c may send data to user device 106-c for
display to
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user 102-c. In some examples, the health monitoring platform 300 may include
any
quantity of users 102, such as User 1 through User N (e.g., with wearable
device 104-n
and user device 106-n). Each user 102 may have any number of wearable devices
104
collecting data, such as sleep data, heart rate data, HRV data, respiratory
rate data,
temperature data, etc.
[0092] The wearable devices 104, user devices 106, or both, may
communicate with
one or more servers 110 and/or an administrator user device 106-a via a
network 108.
For example, the wearable devices 104 may communicate directly with the
servers 110
via network 108 by transmitting data collected from a user 102 directly to the
servers
110 via the network 108. In some other examples, a wearable device 104 may
connect
with a user device 106 (e.g., via Bluetooth) to send the data collected from a
user 102,
where the user device 106 may forward the data to the servers 110 via the
network 108.
For example, wearable device 104-a may collect data from user 102-a (e.g.,
physiological data) and may send the data to the user device 106-a, where the
user
device 106-a may forward the data to the server 110 and/or administrator user
device
106-d via a network 108. The servers 110 may include databases or data stores
(e.g., an
application server, a database server, a cloud-based server, a datacenter, or
a
combination of these or other devices or systems of devices) configured to
store
received data and/or perform the various processing functions described
herein. In this
regard, the server 110 may be used for data storage, management, and
processing.
[0093] In some cases, the users 102 may have access to their own health
related
data, but others may be unaware of potential illness. For example, a wearable
device
104 may collect data that indicates a user 102 may be sick or ill, or
otherwise
experiencing an immunological response (e.g., an increased respiratory rate,
an
increased temperature, increased heart rate, or the like relative to baseline
data for the
user 102). If no preventative measurements are taken by the user 102, the
illness may
spread to other users 102 interacting with the user 102.
[0094] In some examples, the components of the health monitoring platform
300
(e.g., the user device 106-a, the user device 106-b, the user device 106-c,
the servers
110, the administrator user device 106-d) may be configured to acquire
physiological
data from each of the respective users 102, and may calculate scores or
metrics
associated with each respective user 102. Scores/metrics that may be
calculated for each
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respective user 102 may include Sleep Scores, Readiness Scores, health risk
metrics
(e.g., illness risk scores), and the like. In some aspects, scores/metrics may
be calculated
once a day (e.g., once in a 24 hour period), such as when a user 102 accesses
the data
for the first time each day or at a default time each day. Additionally or
alternatively,
the health monitoring platform 300 may be configured to transmit determined
scores/metrics to the administrator user device 106-a at regular or irregular
periodicity
(e.g., once a day). Additional details associated with the calculation of
metrics/scores
for each user 102 (e.g., calculating health risk metrics) are described in
further detail
with respect to FIG. 5.
[0095] In some implementations, physiological data and determined
scores/metrics
(e.g., health risk metrics) for each respective user may be presented to an
administrator
(e.g., doctor, health care professional, coach, manager, employer) via the
administrator
device 106-d in a non-anonymized manner or an anonymized manner. In the
context of
non-anonymized health monitoring, health information (e.g., physiological
data, health
risk scores) may be presented via the administrator user device 106-d along
with
identifiers (e.g., non-anonymized user identifiers) for each respective user.
In other
words, in the context of non-anonymized health monitoring, the administrator
may be
able to know which physiological data and health risk metrics correspond to
each
respective user 102. Comparatively, in the context of anonymized health
monitoring,
health information (e.g., physiological data, health risk scores) may be
presented via the
administrator user device 106-d along with anonymized user identifiers for
each
respective user 102. In other words, in the context of anonymized health
monitoring, the
administrator may the administrator may not know which physiological data and
health
risk metrics correspond to each respective user 102. In this regard,
anonymized health
monitoring techniques described herein may facilitate improved user privacy,
and may
protect user health information.
[0096] In some examples, an administrative personnel operating the
administrator
user device 106-d (e.g., a user of user device 106-d) may set/adjust a
threshold for each
category of collected data, for one or more scores calculated from the
collected data, or
both. For example, an administrator may be able to define (e.g., via the
administrator
user device 106-d) one or more predefined thresholds for evaluating health
risk metrics
determined for the respective users 102. In some other examples, the health
monitoring
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platform 300 (e.g., server 110, administrator user device 106-d) may
select/determine
the one or more predefined thresholds by default, and may communicate the
predefined
thresholds to the administrator user device 106-d. Additionally or
alternatively, the
predefined thresholds may be defined (e.g., preconfigured) at the
administrator user
device 106-d. In some examples, the predefined thresholds may be based on one
or
more uncertainty rates (e.g., false-positive rates, false-negative rates, true-
positive rates,
true-negative rates) related to illness detection for the users 102 of the
health monitoring
platform 300. The selection of predefined thresholds will be described in
further detail
with respect to FIG. 5.
[0097] In cases where collected physiological data and/or determined
scores/metrics
(e.g., health risk metrics) for a user 102 of the health monitoring platform
300 satisfy
one or more predefined thresholds, and thereby indicate one or more potential
health
risks, the health monitoring platform 300 (e.g., server 110, administrator
user device
106-d) may transmit messages or notifications to the user 102 associated with
each of
the one or more potential health risks. For example, if user 102-b exhibits a
health risk
metric above a predefined threshold, the server 110 may send a message to user
device
106-b (e.g., via electronic communication, a push notification to an
application running
at user device 106-b, or the like). Notifications of potential health risks
that may be
transmitted to the respective user device 106-a, the user device 106-b, and
the user
device 106-c, as will be further shown and described with respect to FIG. 7.
In some
cases, messages transmitted to the user device 106-a, the user device 106-b,
and the user
device 106-c may be customizable (e.g., via the administrator user device 106-
d), which
will be described in further detail with respect to FIG. 6. Moreover, an
administrator
associated with the administrator user device 106-d may track whether
respective users
102 have viewed messages related to potential health risks, which will be
described in
further detail with respect to FIG. 4.
[0098] FIG. 4 illustrates an example of a GUI 400 that supports an
anonymized
health monitoring platform in accordance with aspects of the present
disclosure. The
GUI 400 may implement, or be implemented by, aspects of the system 100, system
200,
health monitoring platform 300, or any combination thereof For example, the
GUI 400
may be an example of a GUI of an administrator user device, such as the
administrator
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user device 106-d of the health monitoring platform 300 as described with
reference to
FIG. 3.
[0099] The GUI 400 illustrates an application page 405, that may be
displayed to an
administrator/user via the GUI 400 (e.g., GUI of the administrator user device
106-d).
Continuing with the example above, an administrator associated with a group
410 of
users may check collected data or calculated scores for one or more users in
the group
410. The group 410 may be based on a physical location of one or more users
(e.g., a
floor in an office building), one or more lifestyle parameters for each user
(e.g., users
with known health risks), employer or organization, or the like. The
administrative
personnel may be presented with the application page 405 upon opening a
platform
(e.g., "app") for viewing collected data from wearable devices 104 (e.g.,
rings),
calculated scores based on the collected data, or both, for the group 410. In
some
examples, the data may be collected and the scores may be calculated
periodically, such
as once per calendar day 415 (e.g., every 24 hour period). A system, such as
systems as
described with reference to FIGs. 1 through 3, may automatically generate a
health risk
metric 420, such as a risk score, for at least a subset of users in the group
410 when the
wearable device syncs collected data.
[0100] As shown in FIG. 4, the application page 405 may display one or
more
health risk metrics 420 for each user. As it is used herein, the terms "health
risk metric,"
and like terms, may refer to scores or other metrics that indicate a relative
probability or
likelihood that a given user may experience some health risk (e.g.,
probability that a
user is or will become ill, relative probability that the user will experience
a cardiac
episode). The health risk metrics 420 may include a value between 0 and 100
(e.g., 93),
which a processor of a device, such as a user device, may calculate according
to a
machine learning algorithm or classifier. For example, the machine learning
algorithm
may be trained using a set of training physiological data acquired from
healthy patients
and confirmed sick patients with one or more categories corresponding to the
categories
of data collected by the wearable devices of the users (heart rate data, HRV
data,
temperature data, respiratory rate data, etc.). Each health risk metric 420
may be
calculated using the machine learning algorithm according to precision and
recall curves
using collected data from the wearable device as an input. The application
page 405
may display calculated health risk metrics 420 in a list 425 as well as a
graphical display
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430, which may be a histogram showing the calculated health risk metrics 420
for the
users of the group 410 in ranges. For example, the range may be 0 to 100 with
a step
value of 1 if the health risk metric 420 has a value between 0 and 100. The
application
page 405 may display the health risk metric 420 for each member in the group
410 with
a total member count 435.
[0101] In some aspects, the system may calculate and display health risk
metrics for
multiple illnesses, diseases, or conditions. In this regard, each user may be
associated
with multiple health risk metrics that indicate a relative probability that
the respective
user will experience multiple different health conditions. For example, each
user may be
associated with a first health risk metric associated with the flu, a second
health risk
metric associated with COVID-19, and a third health risk metric associated
with cardiac
episodes. Moreover, in some implementations, each health risk may be
associated with
a different respective threshold used to evaluate the relative health risk
metric for each
user, trigger alerts, and the like. In some cases, the application pages may
display the
health risk metrics for different health conditions on the same page, or may
enable users
to "toggle" between application pages associated with different health
conditions.
[0102] In some examples, the administrative personnel may view a member
identifier (ID) 440 for each user in the group 410 (e.g., a user identity or
an anonymized
user identifier for each respective user). If the member ID 440 includes
identity
information of the user, the administrative personnel may have access to
health data of
the users (e.g., non-anonymized health monitoring). In other words, in the
context of
non-anonymized health monitoring, health risk metrics 420, Sleep/Readiness
Scores,
and/or acquired physiological data may be presented to an administrator along
with non-
anonymized user identifiers such that the administrator may determine which
users
correspond to the respective metrics/scores, acquired data, and the like.
However, if the
administrative personnel is an employer, sharing health data may compromise
privacy
and ethical policies (e.g., by asking a user to share potentially personal
information).
[0103] Thus, as illustrated in the application page 405, the
administrative personnel
may define messaging rules to codify illness protocol for anonymously
contacting at-
risk users. In other words, physiological data and health risk metrics may be
associated
with anonymized user identifiers corresponding to respective users such that
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administrator personnel are not able to determine which users are associated
with
determined health risk metrics, acquired physiological data, and the like
(e.g.,
anonymized health monitoring). Using anonymized user identifiers may help
protect
personal privacy for the respective users, which may be utilized in the
context of
employer-employee relationships (e.g., user-employees and administrator-
employers).
With this approach, the administrative personnel may have access to aggregated
health
and compliance data, but not personal identifiable information for individual
users,
which may reduce privacy and security concerns, and reduce the amount of
manual
work involved in directly contacting potential at-risk users.
[0104] In some examples, one or more users may opt-in to the group 410 by
using a
wearable device that collects health related data (e.g., physiological data,
or biometrics).
In other cases, users may opt-in to the group 410 for health tracking by
inputting a user
input (e.g., command) into a user device 106 associated with the respective
user. For
example, the users 102 of the health monitoring platform may be able to opt-in
to the
health monitoring platform (e.g., opt-in to the group 410) for health tracking
by
inputting one or more opt-in commands via the user devices 106 associated with
each of
the respective users. In some implementations, physiological data may not be
collected
and scores/metrics may not be calculated for a given user until the user has
opted-into
the health monitoring platform.
[0105] In some examples, an administrative personnel (e.g., employer
administrators) may access a dashboard, illustrated by the application page
405, which
illustrates an anonymized list of users arranged by health risk metric 420 for
the
respective users of the group 410 for each calendar day 415. For example, the
health
risk metrics 420 in the list 425 may be sorted from a largest health risk
metric 420 (e.g.,
96) to a smallest health risk metric 420. A member ID 440 may include a unique
code
for each user but may not include information related to the identity of the
user. For
example, as described previously herein, member IDs 440 for the users within
the group
410 may include anonymized user identifiers associated with each respective
user.
[0106] In some cases, the administrative personnel may select a user icon
445
associated with a respective user in order to view more details regarding the
user's
physiological data and/or determined scores/metrics. For example, by selecting
the user
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icon 445, an administrator may be able to view the user's health risk metrics
420 (e.g.,
via a line chart) for some historical time period (e.g., the last two weeks).
Illustrating the
user's historical health risk metrics 420 may enable the administrator to
gauge the
"severity" of the user's health risk metrics 420. In particular, historical
health risk
metrics 420 may enable the administrator to determine whether a health risk
metric 420
for the user is a random one-off metric, or if the user has exhibited a trend
of increasing
health risk metrics. In some implementations, additional health data that is
displayed
upon selection of the user icon 445 may be presented anonymously (e.g., in
association
with an anonymized user identifier for the respective user) such that the
user's identity
remains anonymous. In other cases, the additional health data that is
displayed upon
selection of the user icon 445 may be presented non-anonymously (e.g., in
association
with a non-anonymized user identifier for the respective user) such that the
administrator may determine to which user the data corresponds.
[0107] In some examples, administrative personnel may receive a
notification (e.g.,
via electronic communication) if a user in the group 410 exhibits a health
risk metric
420 that satisfies one or more thresholds 450 (e.g., predefined thresholds
450). As noted
previously herein, the system 200 may utilize or implement different
thresholds 450 for
different illnesses or health risks, and may calculate different health risk
metrics 420 for
the different respective illnesses/health risks. The health monitoring
platform 300 may
contact users that exhibit a health risk metric 420 that satisfies predefined
threshold 450
by sending a message to the users. Additionally or alternatively, the health
monitoring
platform 300 may transmit a notification to the administrator user device 106-
d if a user
exhibits a health risk metric 420 that satisfies a predefined threshold 450.
For example,
as shown in the application page 405, the health monitoring platform may
determine
that eight users exhibit health risk metrics 420 that exceed the predefined
threshold 450.
In this example, the health monitoring platform 300 may transmit a
signal/message to
cause the administrator user device 106-d to display a notification of a
potential health
risk for the eight identified users.
[0108] In some implementations, the messages that are transmitted to the
user
devices 106 of the respective user may be customizable by the administrative
personnel
(e.g., configurable messages). Users with health risk metrics 420 above the
predefined
threshold 450 may be referred to as "at-risk users" for a potential health
risk, while
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users with health risk metrics 420 below the predefined threshold 450 may not
be at
risk. In some cases, the administrative personnel may set the predefined
threshold 450,
which is described in further detail with respect to FIG. 5. In some other
cases, the
predefined threshold 450 may be set to a default health risk metric 420, which
may be
referred to as a baseline threshold (e.g., a score of 90). For example,
according to the
company-defined protocol, administrators may contact users with a health risk
metric
420 above a customer-specific predefined threshold 450 and may inquire about
the
situation of the user. If the administrator decides there may be a risk that
the user is ill,
the user may be sent to receive help from a medical professional (e.g., an
illness testing
kit). The administrative personnel may ask the user to stay away from the rest
of the
group 410 of users until the illness risk is resolved.
[0109] In some examples, upon identifying that a user may exhibit a
potential health
risk, the health monitoring platform may transmit configurable messages to the
user
device 106 corresponding to the respective user, which is further illustrated
with respect
to FIG. 7. In some cases, administrative personnel may be able to see whether
the user
has viewed or otherwise interacted with (e.g., opened, responded to) the
configurable
message. For example, a status icon 455 for the configurable message may
change once
the user views the configurable message. In some cases, the user may
acknowledge the
configurable message, which may also change the status icon 455. If the user
does not
acknowledge or view the configurable message, the administrative personnel may
resend guidance (e.g., resend the configurable message, or transmit a new
configurable
message) or may mark the message as viewed via an action menu 460. In other
words,
the administrator may be able to trigger additional configurable messages that
may be
sent to the user device 106 of the respective user via the action menu 460.
[0110] The application page 405 may display a total member count 435 with
a total
number of users in a group 410 (e.g., N users in the group 410) as well as
percentage of
users at risk, not at risk, and without data (e.g., if a wearable device for
that user is not
synced). For example, the components of the health monitoring platform may
identify
users who have not worn their wearable devices 104 for some time interval that
satisfies
a threshold time interval (e.g., users who have not worn their wearable
devices 104 for
two days). In this example, the health monitoring platform may cause the
administrator
user device to display an indication of the users who have not worn their
wearable
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devices (e.g., "No Wear Members"). In some cases, the health monitoring
platform may
display non-anonymized user identifiers for each of the "No Wear Members" so
that the
administrator may contact the respective users to see why they are not wearing
their
wearable devices 104, or to request that the users wear their respective
wearable devices
104. The health monitoring platform may determine that users are not wearing
their
wearable devices based on physiological data acquired from the respective
users (e.g.,
temperature readings that indicate that the wearable device 104 is not being
worn), an
absence of physiological data (e.g., the wearable device 104 is on the charger
and is not
being worn), or both.
[0111] FIG. 5 illustrates an example of a GUI 500 that supports an
anonymized
health monitoring platform in accordance with aspects of the present
disclosure. The
GUI 500 may implement, or be implemented by, aspects of the system 100, system
200,
health monitoring platform 300, GUI 400, or any combination thereof For
example, the
GUI 500 may be an example of a GUI of an administrator user device, such as
the
administrator user device 106-d of the health monitoring platform 300 as
described with
reference to FIG. 3.
[0112] In some examples, the GUI 500 illustrates a series of application
pages 505
that may be displayed to an administrator/user via the GUI 500 (e.g., GUI of
the
administrator user device). An administrative personnel may adjust one or more
predefined thresholds 510, such as a health risk metric threshold related to
collected
data for one or more users in a group. For example, an administrative
personnel may set
a threshold 510 based on adjusting the threshold 510 according to a sliding
scale 530.
The health risk metrics 515 may be calculated based on a likelihood or risk of
a user to
be sick (e.g., a risk score).
[0113] In some cases, the predefined threshold 510 may be
determined/selected
based on one or more uncertainty rates or illness prediction accuracy metrics
related to
illness detection for a user. Moreover, an application page 505-a may display
an
indication of one or more illness prediction accuracy metrics associated with
the one or
more predefined thresholds 510. Illness prediction accuracy metrics may be
associated
with a false-positive rate (e.g., rate/percentage of users who will
incorrectly be
identified as being sick), a false-negative rate (e.g., rate/percentage of
users who are
actually sick who will be incorrectly identified as being healthy), a true-
positive rate
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(e.g., rate/percentage of users who will correctly be identified as being
sick), a true-
negative rate (e.g., rate/percentage of users who will correctly be identified
as being
healthy), or any combination thereof For instance, the application page 505-a
may
display a false-positive rate 520 and a true-positive rate 525 for the
selected predefined
threshold 510.
[0114] It may be appreciated herein that the illness prediction accuracy
metrics
(e.g., uncertainty rates) may be a function of the selected predefined
threshold 510, and
may therefore change as the administrator changes the threshold 510 via the
sliding
scale 530. For example, if the predefined threshold 510 is decreased, the
false-positive
rate 520 may increase, as more users may exhibit health risk metrics 515 that
satisfy the
decreased threshold 510. By way of another example, if the administrative
personnel
sets the threshold 510 to 90, and 3% of the users or members in the group are
above the
threshold 510, then the 3% may be users with symptoms 535. Of the users above
the
threshold 510, there may be an uncertainty related to the true-positive rate
525 and the
false-positive rate 520. For example, 40% of the users with health risk
metrics 515
above 90 may be true-positive (e.g., may actually be sick), while 60% of the
users with
health risk metrics 515 above 90 may be false-positive. Additionally or
alternatively, the
application page 505-a may display an uncertainty value (e.g., as a
percentage) of false-
negatives and true-negatives for the threshold 510. The application page 505-a
may
display the uncertainty rate and an information message 537 related to the
uncertainty
rate, such that an administrative personnel may make an informed decision when
setting
the threshold 510. For example, the information message 537 may indicate "3%
of your
members are above the selected Risk Score of 90 today. By testing members
above 90,
on average, you will identify approximately 40% of your member population who
are
likely to have symptoms of an infection," and "A Risk Score of 90 will also
include, on
average, 60% false positives ¨ members above the threshold who likely are not
experiencing symptoms of an infection."
[0115] In some cases, as illustrated in application page 505-b, an
administrator may
set/select (e.g., via the administrator user device 106-d) multiple thresholds
510 for
determined health risk metrics 515. The use of multiple predefined thresholds
510 may
be used to classify a group of users into different health risk categories.
Moreover, the
administrator may be able to set different configurable messages 540 that may
be sent to
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the users who satisfy the respective predefined thresholds 510. For example, a
first user
may exhibit a health risk metric 515 between 65 and 85, and a second user may
exhibit
a health risk metric 515 above 85. In this example, the first user may receive
first
configurable message 540-a, and the second user may receive a second
configurable
message 540-b that is different from the first configurable message 540-a. An
administrative personnel may set each of the predefined thresholds 510 based
on (e.g.,
according to) displayed or calculated uncertainty metrics (e.g., false-
positive rate 520,
true-positive rate 525). Moreover, the administrative personnel may input the
configurable messages 540 corresponding to the respective predefined
thresholds 510
via the administrator user device 106-d. These concepts may be further shown
and
described with reference to FIG. 6.
[0116] FIG. 6 illustrates an example of a GUI 600 that supports an
anonymized
health monitoring platform in accordance with aspects of the present
disclosure. The
GUI 600 may implement, or be implemented by, aspects of the system 100, system
200,
health monitoring platform 300, GUI 400, GUI 500, or any combination thereof
For
example, the GUI 600 may be an example of a GUI of an administrator user
device,
which may be an example of the administrator user device 106-d of the health
monitoring p1atform300 as described with reference to FIG. 3.
[0117] In some examples, the GUI 600 illustrates a series of application
pages 605,
such as an application page 605-a and an application page 605-b, which may be
displayed to the user via the GUI 600 (e.g., GUI of the administrator user
device 106-d).
An administrator may input and/or modify one or more configurable messages 610
that
may be transmitted to a user device 106 corresponding to a user. For example,
in cases
where a user of a health monitoring platform exhibits a health risk score that
satisfies a
predefined threshold, the health monitoring platform may transmit a
configurable
message 610 to the user device, where the user and the user device may be
examples of
a user 102 and a user device 106 as described with reference to FIGs. 1
through 3.
[0118] In some cases, an administrator may set one or more predefined
thresholds
615 against which health risk metrics are to be compared. In particular, the
administrator may define multiple predefined thresholds 615, and multiple
configurable
messages 610 that may be transmitted to users upon satisfaction of the
respective
predefined thresholds 615. For example, the health monitoring platform (e.g.,
a server
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110) may be configured to transmit the configurable message 610-a to a user
device
corresponding to a user upon satisfaction of the predefined threshold 615-a,
and may be
configured to transmit the configurable message 610-b upon satisfaction of the
predefined threshold 615-b. In other words, the administrator user device may
be used
to define different configurable messages 610 for each respective predefined
threshold
615.
[0119] The configurable messages 610 may guide users to follow an illness
protocol
for a group. For example, a configurable messages 610-a may indicate that a
potential
health risk has been identified, such as by indicating "your body signals
indicate that
something may be straining your body . . . "based on the threshold 615-a. By
way of
another example, the configurable messages 610-a may provide one or more
recommendations to the user, such as a recommendation for the user to schedule
a
doctor appointment, to stay home from work or other activities, to prepare for
a
potential illness by resting or hydrating, and the like. In some other
examples, an
administrative user may configure a configurable message 610-b to indicate to
the user
"nothing to worry about . . . "based on the threshold 615-b.
[0120] In some examples, the administrator user device 106-d may display
a
guidance icon 620 that indicates whether users who have received the
respective
configurable message 610 have viewed, interacted with, snoozed/dismissed, or
otherwise interacted with the configurable message 610. The application page
605-a and
the application page 605-b may display an active period 625 for the user
and/or the
configurable message 610-a and the configurable message 610-b, respectively,
along
with statistics about how many users were affected and acted on the
configurable
message 610-a and the configurable message 610-b. In some cases, the
application
pages 605 may display aggregated statistics associated with health risk
metrics per
group of users.
[0121] In some cases, users may be able to input information associated
with health
risks via the user devices of the health monitoring platform. For example, a
user may
indicate (via a user device) whether the user took actions (e.g., took a
diagnostic test) to
confirm a potential health risk, or whether a diagnostic test (e.g., flu test,
COVID test)
came back positive or negative.
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[0122] FIG. 7 illustrates an example of a GUI 700 that supports an
anonymized
health monitoring platform in accordance with aspects of the present
disclosure. The
GUI 700 may implement, or be implemented by, aspects of the system 100, system
200,
health monitoring platform 300, GUI 400, GUI 500, GUI 600, or any combination
thereof For example, the GUI 700 may be an example of a GUI of a user device
corresponding to a user, such as a user device 106 and a user 102 as described
with
reference to FIG. 3.
[0123] In some examples, the GUI 700 may illustrate a series of
application pages
705, such as an application page 705-a and an application page 705-b, that may
be
displayed to a user via the GUI 700 of a user device. As noted previously
herein,
physiological data collected from a user may be used to calculate
scores/metrics (e.g.,
health risk scores, Sleep Scores, Readiness Scores) for the respective user.
Calculated
scores/metrics may be displayed to the user via a user device corresponding to
the user,
as shown in the application pages 705.
[0124] In cases where a user's health risk metric satisfies one or more
predefined
thresholds, as described herein, a health monitoring platform, such as a
health
monitoring platform 300 as described with reference to FIG. 3 (e.g., server
110,
administrator user device 106-d), may transmit one or more configurable
messages 710
to the user, where the configurable messages may be associated with potential
health
risks for the user. For example, the user may receive a configurable message
710-a,
which may indicate for the user to check in with a contact person associated
with a
group of users (e.g., administrator of an office). In some other cases, the
user may
receive configurable message 710-b, which may prompt the user to check in for
more
information or dismiss the configurable message 710-b. The configurable
messages 710
may be configurable/customizable, such that the user may receive different
configurable
messages 710 based on different health risk scores, as described previously
herein.
Moreover, an administrator (e.g., administrator associated with the
administrator user
device 106-d) may customize the configurable messages 710 that may be
transmitted to
users within the health monitoring platform.
[0125] By enabling administrators to define rules for sending
configurable messages
710 to users based on their respective health risk metrics, techniques
described herein
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may automate alerts, messages, and other information that may be provided to
users
without divulging sensitive information to employer personnel or other
administrators.
[0126] In some cases, health risk metrics for a user may be determined
based on one
or more physiological parameters (e.g., temperature, heart rate, HRV,
respiratory rate)
being different than a baseline set of values for each respective user (e.g.,
baseline
temperature data, baseline HRV data). For example, the health monitoring
platform may
acquire physiological data from a user (e.g., temperature data, respiratory
rate data,
HRV data) over a time interval (e.g., two weeks) when the user is in a healthy
state. The
health monitoring platform may generate baseline physiological data for the
user (e.g.,
customized baseline ranges) based on the acquired physiological data. For
instance,
acquired physiological data may be used to generate baseline temperature data,
baseline
respiratory rate data, baseline HRV data, and the like. In some aspects,
baseline data
sets may take into account circadian features. For instance, the user may
naturally
exhibit fluctuations in temperature readings throughout the day (e.g.,
throughout a 24
hour circadian cycle), in which case these natural fluctuations are reflected
within the
baseline temperature data.
[0127] Continuing with the same example, the health monitoring platform
may
continually acquire physiological data from the user, and compare collected
parameters
to the user's own baseline parameters. For example, if the user's respiratory
rate or heart
rate is relatively high compared to a baseline respiratory rate or baseline
heart rate for
the user, the user's health risk metric may be higher, which may indicate that
the user is
sick, or likely to become sick (e.g., with the flu or related illness).
Similarly, if there are
one or more trends (e.g., lack of sleep, high blood pressure, low blood
pressure, or the
like) that may affect general health of a user or may indicate the presence of
one or
more preexisting conditions, such as sleep apnea, hypertension, atrial
fibrillation,
diabetes, and more, the user may receive one or more messages to seek medical
advice
(e.g., one per preexisting condition, illness, or health risk).
[0128] In some implementations, the health monitoring platform may
indicate one
or more contributing factors that contribute to a user's health risk metric to
provide
additional insight regarding the user's health risk metric. For example, the
application
pages 705 may indicate one or more physiological parameters (e.g.,
contributing
factors) that resulted in the user's health risk metric, such as increased
temperature,
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increased respiratory rate, increased heart rate, and the like. In other
words, the health
monitoring platform may be configured to provide some information or other
insights
regarding determined health risk metrics. Personalized insights may indicate
aspects of
collected physiological data (e.g., contributing factors within the
physiological data)
that were used to generate the health risk metrics.
[0129] For example, in cases where a user does not experience high
temperatures
(e.g., no fever), but still exhibits a relatively high health risk metric
(e.g., high
probability of illness), providing personalized insights may provide
additional
information that is driving the high health risk metric. Moreover, providing
personalized
insights may drive greater user engagement. Further, personalized insights may
provide
answers to questions such as "What is driving my high health risk metric?",
"Why do I
have a high health risk metric if my temperature is normal?", "Why do I
sometimes see
big changes in my health risk metric (e.g., health resource management (HRM)
score)
from one day to the next?", "Why is my readiness score saying one thing, but
my health
risk metric score is saying something else?", and "I have asthma; is my score
high
because of my respiration rate, or is it picking up on something more?". In
this regard,
insights may explain contributing factors for the health risk metrics without
exposing
raw physiological data, user privacy, or details of the algorithms/models used
to
generate the health risk metrics.
[0130] In some implementations, the health monitoring platform may be
configured
to receive user inputs regarding detected/predicted illness in order to train
classifiers
(e.g., supervised learning for a machine learning classifier) and improve
illness
detection techniques. For example, the user device 106 may display a health
risk metric
indicating a relative likelihood that the user will become ill. Subsequently,
the user may
input one or more user inputs, such as an onset of symptoms, a positive
illness test, and
the like. These user inputs may then be input into the classifier to train the
classifier. In
other words, the user inputs may be used to validate, or confirm, the
determined illness
risk metrics.
[0131] FIG. 8 shows a block diagram 800 of a device 805 that supports an
anonymized health monitoring platform in accordance with aspects of the
present
disclosure. The device 805 may include an input module 810, an output module
815,
and a wearable application 820. The device 805 may also include a processor.
Each of
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these components may be in communication with one another (e.g., via one or
more
buses).
[0132] The input module 810 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 805. The input module 810 may utilize a single
antenna or a
set of multiple antennas.
[0133] The output module 815 may provide a means for transmitting signals
generated by other components of the device 805. For example, the output
module 815
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 815 may be co-located with an input module
810 in
a transceiver module. The output module 815 may utilize a single antenna or a
set of
multiple antennas.
[0134] For example, the wearable application 820 may include a data
acquisition
component 825, a health risk metric component 830, a user interface component
835, or
any combination thereof In some examples, the wearable application 820, 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 810,
the output module 815, or both. For example, the wearable application 820 may
receive
information from the input module 810, send information to the output module
815, or
be integrated in combination with the input module 810, the output module 815,
or both
to receive information, transmit information, or perform various other
operations as
described herein.
[0135] The wearable application 820 may support health monitoring
techniques in
accordance with examples as disclosed herein. The data acquisition component
825 may
be configured as or otherwise support a means for receiving physiological data
associated with one or more users, the physiological data being continuously
collected
via one or more wearable devices associated with the respective one or more
users,
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wherein the one or more users are associated with respective anonymized user
identifiers. The health risk metric component 830 may be configured as or
otherwise
support a means for identifying one or more health risk metrics associated
with each
respective user of the one or more users based at least in part on a
respective subset of
the physiological data associated with each respective user. The health risk
metric
component 830 may be configured as or otherwise support a means for
identifying a
potential health risk for at least one anonymized user identifier
corresponding to at least
one user based at least in part on the one or more health risk metrics
associated with the
at least one user satisfying one or more predefined thresholds. The user
interface
component 835 may be configured as or otherwise support a means for causing a
GUI
of an administrator user device to display a notification of the potential
health risk
associated with the at least one anonymized user identifier. The user
interface
component 835 may be configured as or otherwise support a means for causing an
additional GUI of an additional user device corresponding to the at least one
user to
display a configurable message associated with the potential health risk.
[0136] FIG. 9 shows a block diagram 900 of a wearable application 920
that
supports an anonymized health monitoring platform in accordance with aspects
of the
present disclosure. The wearable application 920 may be an example of aspects
of a
wearable application or a wearable application 820, or both, as described
herein. The
wearable application 920, or various components thereof, may be an example of
means
for performing various aspects of an anonymized health monitoring platform as
described herein. For example, the wearable application 920 may include a data
acquisition component 925, a health risk metric component 930, a user
interface
component 935, a user input component 940, a wearable device adherence
component
945, a communications component 950, or any combination thereof Each of these
components may communicate, directly or indirectly, with one another (e.g.,
via one or
more buses).
[0137] The wearable application 920 may support health monitoring
techniques in
accordance with examples as disclosed herein. The data acquisition component
925 may
be configured as or otherwise support a means for receiving physiological data
associated with one or more users, the physiological data being continuously
collected
via one or more wearable devices associated with the respective one or more
users,
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wherein the one or more users are associated with respective anonymized user
identifiers. The health risk metric component 930 may be configured as or
otherwise
support a means for identifying one or more health risk metrics associated
with each
respective user of the one or more users based at least in part on a
respective subset of
the physiological data associated with each respective user. In some examples,
the
health risk metric component 930 may be configured as or otherwise support a
means
for identifying a potential health risk for at least one anonymized user
identifier
corresponding to at least one user based at least in part on the one or more
health risk
metrics associated with the at least one user satisfying one or more
predefined
thresholds. The user interface component 935 may be configured as or otherwise
support a means for causing a GUI of an administrator user device to display a
notification of the potential health risk associated with the at least one
anonymized user
identifier. In some examples, the user interface component 935 may be
configured as or
otherwise support a means for causing an additional GUI of an additional user
device
corresponding to the at least one user to display a configurable message
associated with
the potential health risk.
[0138] In some examples, the user input component 940 may be configured
as or
otherwise support a means for receiving, via the administrator user device, a
user input
comprising the configurable message, wherein causing the additional user
device to
display the configurable message is based at least in part on receiving the
user input.
[0139] In some examples, the user input component 940 may be configured
as or
otherwise support a means for receiving, via the administrator user device, a
user input
comprising the one or more predefined thresholds, wherein identifying the
potential
health risk for the at least one anonymized user identifier is based at least
in part on
receiving the user input.
[0140] In some examples, the user input component 940 may be configured
as or
otherwise support a means for receiving, via the administrator user device, a
first user
input comprising a first predefined threshold and a second predefined
threshold, the first
and second predefined thresholds being included within the one or more
predefined
thresholds. In some examples, the user input component 940 may be configured
as or
otherwise support a means for receiving, via the administrator user device, a
second
user input comprising a first configurable message associated with
satisfaction of the
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first predefined threshold and a second configurable message associated with
satisfaction of the second predefined threshold.
[0141] In some examples, to support causing the additional user device to
display
the configurable message, the user interface component 935 may be configured
as or
otherwise support a means for causing the additional GUI of the additional
user device
to display the first configurable message based at least in part on
identifying that the one
or more health risk metrics associated with the at least one user satisfy the
first
predefined threshold. In some examples, to support causing the additional user
device to
display the configurable message, the user interface component 935 may be
configured
as or otherwise support a means for causing the additional GUI of the
additional user
device to display the second configurable message based at least in part on
identifying
that the one or more health risk metrics associated with the at least one user
satisfy the
second predefined threshold.
[0142] In some examples, the user interface component 935 may be
configured as
or otherwise support a means for causing the GUI of the administrator user
device to
display an indication of one or more illness prediction accuracy metrics
associated with
the one or more predefined thresholds, the one or more illness prediction
accuracy
metrics associated with a false-positive rate for the one or more predefined
thresholds, a
true-positive rate for the one or more predefined thresholds, a false-negative
rate for the
one more predefined thresholds, a true-negative rate for the one or more
predefined
thresholds, or any combination thereof
[0143] In some examples, to support causing the administrator device to
display the
notification, the user interface component 935 may be configured as or
otherwise
support a means for causing the GUI of the administrator user device to
display an
indication of the at least one anonymized user identifier and the one or more
health risk
metrics associated with the user corresponding to the at least one anonymized
user
identifier.
[0144] In some examples, the user interface component 935 may be
configured as
or otherwise support a means for causing the GUI of the administrator user
device to
display an indication that the configurable message has been provided to the
additional
user device corresponding to the at least one user.
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[0145] In some examples, the user interface component 935 may be
configured as
or otherwise support a means for causing the GUI of the administrator user
device to
display an indication that the at least one user has not viewed or interacted
with the
configurable message.
[0146] In some examples, the user interface component 935 may be
configured as
or otherwise support a means for causing the GUI of the additional user device
to
display a second configurable message associated with the potential health
risk based at
least in part on the indication that the at least one user has not viewed or
interacted with
the configurable message.
[0147] In some examples, the user input component 940 may be configured
as or
otherwise support a means for receiving, via the administrator user device, a
user input
to trigger the second configurable message, wherein causing the GUI of the
additional
user device to display the second configurable message is based at least in
part on
receiving the user input.
[0148] In some examples, the wearable device adherence component 945 may
be
configured as or otherwise support a means for identifying a second user of
the one or
more users who has not worn the respective wearable device associated with the
second
user for a time interval that satisfies a threshold time interval. In some
examples, the
user interface component 935 may be configured as or otherwise support a means
for
causing a GUI of the administrator user device to display a non-anonymized
user
identifier associated with the second user based at least in part on
identifying that the
second user has not worn the respective wearable device for the time interval.
[0149] In some examples, identifying the second user of the one or more
users who
has not worn the respective wearable device is based at least in part on a
portion of the
physiological data received from the respective wearable device associated
with the
second user, an absence of physiological data received from the respective
wearable
device associated with the second user, or both.
[0150] In some examples, the user input component 940 may be configured
as or
otherwise support a means for receiving one or more opt-in commands from the
one or
more user devices associated with the respective one or more users, wherein
receiving
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the physiological data associated with the one or more users is based at least
in part on
receiving the one or more opt-in commands.
[0151] In some examples, the health risk metric component 930 may be
configured
as or otherwise support a means for identifying one or more contributing
factors for the
potential health risk for the at least one anonymized user identifier. In some
examples,
the user interface component 935 may be configured as or otherwise support a
means
for causing the GUI of the administrator user device, the additional GUI of
the
additional user device, or both, to display an indication of the one or more
contributing
factors.
[0152] In some examples, to support causing the administrator user device
to
display the notification of the potential health risk associated with the at
least one
anonymized user identifier, the communications component 950 may be configured
as
or otherwise support a means for transmitting the notification to the
administrator user
device, wherein the notification comprises an email, a text message, a push
notification,
or any combination thereof
[0153] FIG. 10 shows a diagram of a system 1000 including a device 1005
that
supports an anonymized health monitoring platform in accordance with aspects
of the
present disclosure. The device 1005 may be an example of or include the
components of
a device 805 as described herein. In some implementations, the device 1005 may
include an example of a user device 106 and/or server 110, as described
herein. The
device 1005 may include components for bi-directional communications with
components described herein (e.g., ring 104, server 110, user device 106),
such as a
wearable application 1020, a communication module 1010, an antenna 1015, a
user
interface component 1025, a database (application data) 1030, a memory 1035,
and a
processor 1040. These components may be in electronic communication or
otherwise
coupled (e.g., operatively, communicatively, functionally, electronically,
electrically)
via one or more buses (e.g., a bus 1045).
[0154] The communication module 1010 may manage input and output signals
for
the device 1005 via the antenna 1015. The communication module 1010 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 1010 may manage
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communications with the ring 104 and the server 110, as illustrated in FIG. 2.
The
communication module 1010 may also manage peripherals not integrated into the
device 1005. In some cases, the communication module 1010 may represent a
physical
connection or port to an external peripheral. In some cases, the communication
module
1010 may utilize an operating system such as i0S0, ANDROID , MS-DOS , MS-
WINDOWS , OS/20, UNIX , LINUX , or another known operating system. In other
cases, the communication module 1010 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 1010 may be implemented as part of the
processor 1040. In some examples, a user may interact with the device 1005 via
the
communication module 1010, user interface component 1025, or via hardware
components controlled by the communication module 1010.
[0155] In some cases, the device 1005 may include a single antenna 1015.
However,
in some other cases, the device 1005 may have more than one antenna 1015,
which may
be capable of concurrently transmitting or receiving multiple wireless
transmissions.
The communication module 1010 may communicate bi-directionally, via the one or
more antennas 1015, wired, or wireless links as described herein. For example,
the
communication module 1010 may represent a wireless transceiver and may
communicate bi-directionally with another wireless transceiver. The
communication
module 1010 may also include a modem to modulate the packets, to provide the
modulated packets to one or more antennas 1015 for transmission, and to
demodulate
packets received from the one or more antennas 1015.
[0156] The user interface component 1025 may manage data storage and
processing
in a database1030. In some cases, a user may interact with the user interface
component
1025. In other cases, the user interface component 1025 may operate
automatically
without user interaction. The database 1030 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.
[0157] The memory 1035 may include RAM and ROM. The memory 1035 may
store computer-readable, computer-executable software including instructions
that,
when executed, cause the processor 1040 to perform various functions described
herein.
In some cases, the memory 1035 may contain, among other things, a basic I/O
system
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(BIOS) which may control basic hardware or software operation such as the
interaction
with peripheral components or devices.
[0158] The processor 1040 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), a
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 1040 may be configured to operate a
memory
array using a memory controller. In other cases, a memory controller may be
integrated
into the processor 1040. The processor 1040 may be configured to execute
computer-
readable instructions stored in a memory 1035 to perform various functions
(e.g.,
functions or tasks supporting a method and system for sleep staging
algorithms).
[0159] The wearable application 1020 may support health monitoring
techniques in
accordance with examples as disclosed herein. For example, the wearable
application
1020 may be configured as or otherwise support a means for receiving
physiological
data associated with one or more users, the physiological data being
continuously
collected via one or more wearable devices associated with the respective one
or more
users, wherein the one or more users are associated with respective anonymized
user
identifiers. The wearable application 1020 may be configured as or otherwise
support a
means for identifying one or more health risk metrics associated with each
respective
user of the one or more users based at least in part on a respective subset of
the
physiological data associated with each respective user. The wearable
application 1020
may be configured as or otherwise support a means for identifying a potential
health
risk for at least one anonymized user identifier corresponding to at least one
user based
at least in part on the one or more health risk metrics associated with the at
least one
user satisfying one or more predefined thresholds. The wearable application
1020 may
be configured as or otherwise support a means for causing a GUI of an
administrator
user device to display a notification of the potential health risk associated
with the at
least one anonymized user identifier. The wearable application 1020 may be
configured
as or otherwise support a means for causing an additional GUI of an additional
user
device corresponding to the at least one user to display a configurable
message
associated with the potential health risk.
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[0160] By including or configuring the wearable application 1020 in
accordance
with examples as described herein, the device 1005 may support techniques for
improved illness detection/prediction. In particular, techniques described
herein may
enable a health monitoring platform that is configured to alert users and/or
administrators that a user may transition from a healthy state to an unhealthy
state. As
such, techniques described herein may reduce a spread of illness, and reduce a
severity
of illness. Moreover, techniques described herein may enable anonymized health
monitoring, which may improve user privacy and protect user health data.
[0161] FIG. 11 shows a flowchart illustrating a method 1100 that supports
an
anonymized health monitoring platform 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 10.
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.
[0162] At 1105, the method may include receiving physiological data
associated
with one or more users, the physiological data being continuously collected
via one or
more wearable devices associated with the respective one or more users,
wherein the
one or more users are associated with respective anonymized user identifiers.
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 data
acquisition component 925 as described with reference to FIG. 9.
[0163] At 1110, the method may include identifying one or more health
risk metrics
associated with each respective user of the one or more users based at least
in part on a
respective subset of the physiological data associated with each respective
user. The
operations of 1110 may be performed in accordance with examples as disclosed
herein.
In some examples, aspects of the operations of 1110 may be performed by a
health risk
metric component 930 as described with reference to FIG. 9.
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[0164] At 1115, the method may include identifying a potential health
risk for at
least one anonymized user identifier corresponding to at least one user based
at least in
part on the one or more health risk metrics associated with the at least one
user
satisfying one or more predefined thresholds. 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 health risk metric component 930 as
described with reference to FIG. 9.
[0165] At 1120, the method may include causing a GUI of an administrator
user
device to display a notification of the potential health risk associated with
the at least
one anonymized user identifier. 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 user interface component 935 as
described
with reference to FIG. 9.
[0166] At 1125, the method may include causing an additional GUI of an
additional
user device corresponding to the at least one user to display a configurable
message
associated with the potential health risk. 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 user interface component 935 as
described
with reference to FIG. 9.
[0167] FIG. 12 shows a flowchart illustrating a method 1200 that supports
an
anonymized health monitoring platform in accordance with aspects of the
present
disclosure. The operations of the method 1200 may be implemented by a User
Device
or its components as described herein. For example, the operations of the
method 1200
may be performed by a User Device as described with reference to FIGs. 1
through 10.
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.
[0168] At 1205, the method may include receiving physiological data
associated
with one or more users, the physiological data being continuously collected
via one or
more wearable devices associated with the respective one or more users,
wherein the
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one or more users are associated with respective anonymized user identifiers.
The
operations of 1205 may be performed in accordance with examples as disclosed
herein.
In some examples, aspects of the operations of 1205 may be performed by a data
acquisition component 925 as described with reference to FIG. 9.
[0169] At 1210, the method may include receiving, via an administrator
user device,
a user input comprising the configurable message. The operations of 1210 may
be
performed in accordance with examples as disclosed herein. In some examples,
aspects
of the operations of 1210 may be performed by a user input component 940 as
described
with reference to FIG. 9.
[0170] At 1215, the method may include identifying one or more health
risk metrics
associated with each respective user of the one or more users based at least
in part on a
respective subset of the physiological data associated with each respective
user. The
operations of 1215 may be performed in accordance with examples as disclosed
herein.
In some examples, aspects of the operations of 1215 may be performed by a
health risk
metric component 930 as described with reference to FIG. 9.
[0171] At 1220, the method may include identifying a potential health
risk for at
least one anonymized user identifier corresponding to at least one user based
at least in
part on the one or more health risk metrics associated with the at least one
user
satisfying one or more predefined thresholds. The operations of 1220 may be
performed
in accordance with examples as disclosed herein. In some examples, aspects of
the
operations of 1220 may be performed by a health risk metric component 930 as
described with reference to FIG. 9.
[0172] At 1225, the method may include causing a GUI of the administrator
user
device to display a notification of the potential health risk associated with
the at least
one anonymized user identifier. The operations of 1225 may be performed in
accordance with examples as disclosed herein. In some examples, aspects of the
operations of 1225 may be performed by a user interface component 935 as
described
with reference to FIG. 9.
[0173] At 1230, the method may include causing an additional GUI of an
additional
user device corresponding to the at least one user to display a configurable
message
associated with the potential health risk. The operations of 1230 may be
performed in
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accordance with examples as disclosed herein. In some examples, aspects of the
operations of 1230 may be performed by a user interface component 935 as
described
with reference to FIG. 9.
[0174] FIG. 13 shows a flowchart illustrating a method 1300 that supports
an
anonymized health monitoring platform in accordance with aspects of the
present
disclosure. The operations of the method 1300 may be implemented by a User
Device
or its components as described herein. For example, the operations of the
method 1300
may be performed by a User Device as described with reference to FIGs. 1
through 10.
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.
[0175] At 1305, the method may include receiving physiological data
associated
with one or more users, the physiological data being continuously collected
via one or
more wearable devices associated with the respective one or more users,
wherein the
one or more users are associated with respective anonymized user identifiers.
The
operations of 1305 may be performed in accordance with examples as disclosed
herein.
In some examples, aspects of the operations of 1305 may be performed by a data
acquisition component 925 as described with reference to FIG. 9.
[0176] At 1310, the method may include receiving, via the administrator
user
device, a user input comprising the one or more predefined thresholds. The
operations
of 1310 may be performed in accordance with examples as disclosed herein. In
some
examples, aspects of the operations of 1310 may be performed by a user input
component 940 as described with reference to FIG. 9.
[0177] At 1315, the method may include identifying one or more health
risk metrics
associated with each respective user of the one or more users based at least
in part on a
respective subset of the physiological data associated with each respective
user. The
operations of 1315 may be performed in accordance with examples as disclosed
herein.
In some examples, aspects of the operations of 1315 may be performed by a
health risk
metric component 930 as described with reference to FIG. 9.
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[0178] At 1320, the method may include identifying a potential health
risk for at
least one anonymized user identifier corresponding to at least one user based
at least in
part on the one or more health risk metrics associated with the at least one
user
satisfying one or more predefined thresholds, wherein identifying the
potential health
risk for the at least one anonymized user identifier is based at least in part
on receiving
the user input. The operations of 1320 may be performed in accordance with
examples
as disclosed herein. In some examples, aspects of the operations of 1320 may
be
performed by a health risk metric component 930 as described with reference to
FIG. 9.
[0179] At 1325, the method may include causing a GUI of the administrator
user
device to display a notification of the potential health risk associated with
the at least
one anonymized user identifier. The operations of 1325 may be performed in
accordance with examples as disclosed herein. In some examples, aspects of the
operations of 1325 may be performed by a user interface component 935 as
described
with reference to FIG. 9.
[0180] At 1330, the method may include causing an additional GUI of an
additional
user device corresponding to the at least one user to display a configurable
message
associated with the potential health risk. The operations of 1330 may be
performed in
accordance with examples as disclosed herein. In some examples, aspects of the
operations of 1330 may be performed by a user interface component 935 as
described
with reference to FIG. 9.
[0181] The following provides an overview of the present disclosure:
[0182] A method for health monitoring is described. The method may
include
receiving physiological data associated with one or more users, the
physiological data
being continuously collected via one or more wearable devices associated with
each
respective user of the one or more users, wherein the one or more users are
associated
with respective anonymized user identifiers, identifying one or more health
risk metrics
associated with the each respective user of the one or more users based at
least in part
on a respective subset of the physiological data associated with the each
respective user,
identifying a potential health risk for at least one anonymized user
identifier
corresponding to at least one user based at least in part on the one or more
health risk
metrics associated with the at least one user satisfying one or more
predefined
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thresholds, causing a GUI of an administrator user device to display a
notification of the
potential health risk associated with the at least one anonymized user
identifier, and
causing an additional GUI of an additional user device corresponding to the at
least one
user to display a configurable message associated with the potential health
risk.
[0183] An apparatus for health monitoring 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 one or more users, the physiological data
being
continuously collected via one or more wearable devices associated with each
respective user of the one or more users, wherein the one or more users are
associated
with respective anonymized user identifiers, identify one or more health risk
metrics
associated with the each respective user of the one or more users based at
least in part
on a respective subset of the physiological data associated with the each
respective user,
identify a potential health risk for at least one anonymized user identifier
corresponding
to at least one user based at least in part on the one or more health risk
metrics
associated with the at least one user satisfying one or more predefined
thresholds, cause
a GUI of an administrator user device to display a notification of the
potential health
risk associated with the at least one anonymized user identifier, and cause an
additional
GUI of an additional user device corresponding to the at least one user to
display a
configurable message associated with the potential health risk.
[0184] Another apparatus for health monitoring is described. The
apparatus may
include means for receiving physiological data associated with one or more
users, the
physiological data being continuously collected via one or more wearable
devices
associated with each respective user of the one or more users, wherein the one
or more
users are associated with respective anonymized user identifiers, means for
identifying
one or more health risk metrics associated with the each respective user of
the one or
more users based at least in part on a respective subset of the physiological
data
associated with the each respective user, means for identifying a potential
health risk for
at least one anonymized user identifier corresponding to at least one user
based at least
in part on the one or more health risk metrics associated with the at least
one user
satisfying one or more predefined thresholds, means for causing a GUI of an
administrator user device to display a notification of the potential health
risk associated
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57
with the at least one anonymized user identifier, and means for causing an
additional
GUI of an additional user device corresponding to the at least one user to
display a
configurable message associated with the potential health risk.
[0185] A non-transitory computer-readable medium storing code for health
monitoring is described. The code may include instructions executable by a
processor to
receive physiological data associated with one or more users, the
physiological data
being continuously collected via one or more wearable devices associated with
each
respective user of the one or more users, wherein the one or more users are
associated
with respective anonymized user identifiers, identify one or more health risk
metrics
associated with the each respective user of the one or more users based at
least in part
on a respective subset of the physiological data associated with the each
respective user,
identify a potential health risk for at least one anonymized user identifier
corresponding
to at least one user based at least in part on the one or more health risk
metrics
associated with the at least one user satisfying one or more predefined
thresholds, cause
a GUI of an administrator user device to display a notification of the
potential health
risk associated with the at least one anonymized user identifier, and cause an
additional
GUI of an additional user device corresponding to the at least one user to
display a
configurable message associated with the potential health risk.
[0186] Some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein may further include operations, features,
means, or
instructions for receiving, via the administrator user device, a user input
comprising the
configurable message, wherein causing the additional user device to display
the
configurable message may be based at least in part on receiving the user
input.
[0187] Some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein may further include operations, features,
means, or
instructions for receiving, via the administrator user device, a user input
comprising the
one or more predefined thresholds, wherein identifying the potential health
risk for the
at least one anonymized user identifier may be based at least in part on
receiving the
user input.
[0188] Some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein may further include operations, features,
means, or
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instructions for receiving, via the administrator user device, a first user
input comprising
a first predefined threshold and a second predefined threshold, wherein the
first
predefined threshold and the second predefined threshold may be included
within the
one or more predefined thresholds and receiving, via the administrator user
device, a
second user input comprising a first configurable message associated with
satisfaction
of the first predefined threshold and a second configurable message associated
with
satisfaction of the second predefined threshold.
[0189] In some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein, causing the additional user device to
display the
configurable message may include operations, features, means, or instructions
for
causing the additional GUI of the additional user device to display the first
configurable
message based at least in part on identifying that the one or more health risk
metrics
associated with the at least one user satisfy the first predefined threshold
and causing the
additional GUI of the additional user device to display the second
configurable message
based at least in part on identifying that the one or more health risk metrics
associated
with the at least one user satisfy the second predefined threshold.
[0190] Some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein may further include operations, features,
means, or
instructions for causing the GUI of the administrator user device to display
an indication
of one or more illness prediction accuracy metrics associated with the one or
more
predefined thresholds, the one or more illness prediction accuracy metrics
associated
with a false-positive rate for the one or more predefined thresholds, a true-
positive rate
for the one or more predefined thresholds, a false-negative rate for the one
or more
predefined thresholds, a true-negative rate for the one or more predefined
thresholds, or
any combination thereof
[0191] In some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein, causing the administrator user device to
display the
notification may include operations, features, means, or instructions for
causing the GUI
of the administrator user device to display an indication of the at least one
anonymized
user identifier and the one or more health risk metrics associated with a user
corresponding to the at least one anonymized user identifier.
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[0192] Some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein may further include operations, features,
means, or
instructions for causing the GUI of the administrator user device to display
an indication
that the configurable message may have been provided to the additional user
device
corresponding to the at least one user.
[0193] Some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein may further include operations, features,
means, or
instructions for causing the GUI of the administrator user device to display
an indication
that the at least one user may have not viewed or interacted with the
configurable
message.
[0194] Some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein may further include operations, features,
means, or
instructions for causing the GUI of the additional user device to display a
second
configurable message associated with the potential health risk based at least
in part on
the indication that the at least one user may have not viewed or interacted
with the
configurable message.
[0195] Some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein may further include operations, features,
means, or
instructions for receiving, via the administrator user device, a user input to
trigger the
second configurable message, wherein causing the GUI of the additional user
device to
display the second configurable message may be based at least in part on
receiving the
user input.
[0196] Some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein may further include operations, features,
means, or
instructions for identifying a second user of the one or more users who may
have not
worn a respective wearable device associated with the second user for a time
interval
that satisfies a threshold time interval and causing the GUI of the
administrator user
device to display a non-anonymized user identifier associated with the second
user
based at least in part on identifying that the second user may have not worn
the
respective wearable device for the time interval.
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[0197] Some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein may further include operations, features,
means, or
instructions for identifying the second user of the one or more users who may
have not
worn the respective wearable device may be based at least in part on a portion
of the
physiological data received from the respective wearable device associated
with the
second user, an absence of physiological data received from the respective
wearable
device associated with the second user, or both.
[0198] Some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein may further include operations, features,
means, or
instructions for receiving one or more opt-in commands from one or more user
devices
associated with respective one or more users, wherein receiving the
physiological data
associated with the one or more users may be based at least in part on
receiving the one
or more opt-in commands.
[0199] 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 contributing factors for the
potential health risk
for the at least one anonymized user identifier and causing the GUI of the
administrator
user device, the additional GUI of the additional user device, or both, to
display an
indication of the one or more contributing factors.
[0200] In some examples of the method, apparatuses, and non-transitory
computer-
readable medium described herein, causing the administrator user device to
display the
notification of the potential health risk associated with the at least one
anonymized user
identifier may include operations, features, means, or instructions for
transmitting the
notification to the administrator user device, wherein the notification
comprises an
email, a text message, a push notification, or any combination thereof
[0201] 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.
[0202] The description set forth herein, in connection with the appended
drawings,
describes example configurations and does not represent all the examples that
may be
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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.
[0203] 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.
[0204] 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
[0205] 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
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).
[0206] The functions described herein may be implemented in hardware,
software
executed by a processor, firmware, or any combination thereof If implemented
in
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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."
[0207] 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 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
CA 03223559 2023-12-13
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63
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.
[0208] 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.
