Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
- 1 -
ANXIETY DETECTION IN DIFFERENT USER STATES
FIELD
[0001] The present disclosure relates to methods and systems for
detection
of anxiety in the context of different user states, including states (e.g.,
presence of
motion) that cause physiological arousal not related to anxiety.
BACKGROUND
[0002] Anxiety is a significant clinical concern in autism spectrum
disorder
(ASD) due to its negative impact on physical and psychological health. For
example,
up to 85% of children with ASD experience clinically-significant symptoms of
anxiety [3]. Anxiety in ASD is a significant clinical concern as it can
further
exacerbate core symptoms and increase functional impairments [4]. Treatment of
anxiety in ASD is a challenge. Traditional approaches to anxiety treatment
rely on
self-awareness of anxiety symptoms - an area of significant difficulty in ASD
[5].
This is a barrier to treatment as symptom awareness is key to timely and
effective
application of management strategies.
[0003] Physiological signals offer an opportunity to address the
above
challenge. In particular, physiological signals collected through non-invasive
and
commercially-available wearable sensors can provide a real-time, objective,
and
language-free measure of anxiety states [6]. A technical challenge in
developing an
anxiety detection system is modelling baseline physiological characteristics
of users
and identifying significant changes from this baseline that correspond to
anxiety
states. To this end, supervised and unsupervised learners such as K-Nearest
Neighbours (KNN), Regression Tress (RT), Bayesian Network (BNT), support
vector
machines (SVM), and adaptive filters have been used to detect anxiety states
using
physiological signals [7], [15]-[18]. A limitation of these approaches is that
physiological arousal is not specific to anxiety and may be associated with
other
user states such as physical activity. This results in false positives which
hinder the
real-world operation of existing anxiety detection systems.
CA 3033804 2019-02-14
- 2 -
[0004] Thus, there exists a need to provide an approach for real-time
detection of anxiety in different user states, including user states that may
cause
physiological arousal not related to anxiety.
SUMMARY
[0005] The present disclosure describes examples for real-time
detection of
anxiety, which may also mitigate against false positives due to physical
activity
effects. The examples disclosed herein may enable realization of physiological
anxiety detection methods and systems in naturalistic settings and/or in a
user's
day-to-day life. Examples of the present disclosure may be implemented using
wearables and mobile computing platforms, including currently available
consumer
electronics.
[0006] In some examples, the present disclosure describes an approach
that
uses a multiple model Kalman-like filter to account for different user states.
For
example, in order to account for user motion, the multiple model Kalman-like
filter
proposed may integrate heart rate and accelerometry signals, by tracking user
heart rate under different motion assumptions, and determining the appropriate
model for anxiety detection based on user motion conditions. Evaluation of an
example implementation found a reduction in false positives compared to the
state-
of-the-art, and an overall arousal detection accuracy of 91%.
[0007] In some aspects, the present disclosure describes a system for
providing output based on detection of anxiety in a subject. The system
includes an
output device for providing output dependent on an anxiety indication, the
anxiety
indication representing a current or expected level of anxiety in the subject.
The
system also includes a memory and a processor coupled to the output device and
the memory. The processor is configured to execute computer-executable
instructions to cause the system to: receive at least one physiological
signal, from a
first sensor, the physiological signal representing physiological information
from the
subject; receive at least one context signal; implement a user state detector
to
determine a current user state from a plurality of possible user states, based
on the
at least one context signal; implement an interactive multiple model (IMM)
filter to
determine, using the physiological signal, a respective statistical prediction
of
CA 3033804 2019-02-14
- 3 -
anxiety in each of the plurality of possible user states; and implement an
anxiety
detector to output the anxiety indication, based on a weighting of the
respective
statistical predictions using the determined current user state.
[0008] In any of the above, the instructions, when executed, may
further
cause the system to: implement a feature extractor to: extract the at least
one
physiological feature from the at least one physiological signal, the at least
one
physiological feature being affected by the level of anxiety in the subject;
and
extract the at least one context feature from the at least one context signal,
the at
least one context feature being relevant to determination of the current user
state.
The user state detector may determine the current user state based on the at
least
one context feature extracted from the at least one context signal. The IMM
filter
may determine the respective statistical predictions based on the at least one
physiological feature extracted from the at least one physiological signal.
[0009] In any of the above, the instructions, when executed, may
further
cause the system to implement the feature extractor to: extract the at least
one
physiological feature by calculating a trend using a first defined smoothing
window
length; and extract the at least one context feature by calculating a moving
standard deviation using a second defined smoothing window length.
[0010] In any of the above, the at least one physiological signal may
include a
heart rate signal, the at least one context signal may include an acceleration
signal,
and the plurality of possible user states may include a first user state where
the
user is in motion and a second user state where the user is not in motion.
[0011] In any of the above, the system may also include a heart rate
monitor
for generating the heart rate signal, and an accelerometer for generating the
acceleration signal.
[0012] In any of the above, the instructions, when executed, may
further
cause the system to implement the user state detector to: determine the
current
user state using a modified Kalman filter.
[0013] In any of the above, the instructions, when executed, may
further
cause the system to implement the IMM filter to: determine the respective
CA 3033804 2019-02-14
- 4 -
statistical prediction of anxiety using a respective modified Kalman filter
matched to
each respective possible user state.
[0014] In any of the above, at least one of the at least one context
signal may
be received from a context sensor of the system.
[0015] In any of the above, at least one of the at least one context signal
may
be received from an external system.
[0016] In any of the above, the output device may be a display screen
and
the provided output may be a visual output that is responsive to the current
or
expected level of anxiety in the subject.
[0017] In any of the above, the system may be implemented in a portable
electronic device.
[0018] In any of the above, the system may be implemented in a
wearable
electronic device.
[0019] In any of the above, the system may be implemented in a
virtual
reality device.
[0020] In any of the above, the instructions may be executable by the
processor via cloud computing.
[0021] In any of the above, the instructions may be executable by the
processor via an application programming interface (API) on a server.
[0022] In some aspects, the present disclosure describes a method,
implemented in an electronic device, for providing output based on detection
of
anxiety in a subject. The method includes: receiving at least one
physiological
signal, from a first sensor coupled to the electronic device, the
physiological signal
representing physiological information from the subject; receiving at least
one
context signal; implementing, in the electronic device, a user state detector
to
determine a current user state from a plurality of possible user states, based
on the
at least one context signal; implementing, in the electronic device, an
interactive
multiple model (IMM) filter to determine, using the physiological signal, a
respective statistical prediction of anxiety in each of the plurality of
possible user
CA 3033804 2019-02-14
- 5 -
states; implementing, in the electronic device, an anxiety detector to output
an
anxiety indication, based on a weighting of the respective statistical
predictions
using the determined current user state, the anxiety indication representing a
current or expected level of anxiety in the subject; and providing output, via
an
output device of the electronic device, dependent on the anxiety indication.
[0023] In any of the above, the method may also include implementing,
in the
electronic device, a feature extractor to: extract the at least one
physiological
feature from the .at least one physiological signal, the at least one
physiological
feature being affected by the level of anxiety in the subject; and extract the
at least
one context feature from the at least one context signal, the at least one
context
feature being relevant to determination of the current user state. The user
state
detector may determine the current user state based on the at least one
context
feature extracted from the at least one context signal. The IMM filter may
determine the respective statistical predictions based on the at least one
physiological feature extracted from the at least one physiological signal.
[0024] In any of the above, the at least one physiological signal may
include a
heart rate signal received from a heart rate sensor coupled to the electronic
device,
the at least one context signal may include an acceleration signal received
from an
accelerometer coupled to the electronic device, and the plurality of possible
user
states may include a first user state where the user is in motion and a second
user
state where the user is not in motion.
[0025] In any of the above, the user state detector may determine the
current user state using a modified Kalman filter.
[0026] In any of the above, the IMM filter may determine the
respective
statistical prediction of anxiety using a respective modified Kalman filter
matched to
each respective possible user state.
BRIEF DESCRIPTION OF THE DRAWINGS
CA 3033804 2019-02-14
- 6 -
[0027] Reference will now be made, by way of example, to the
accompanying
drawings which show example embodiments of the present application, and in
which:
[0028] FIG. 1 is a chart illustrating the effect of physical activity
on heart
rate;
[0029] FIG. 2 shows example equations for a single-model modified
Kalman
filter for anxiety detection;
[0030] FIG. 3A is a block diagram illustrating an example disclosed
system for
anxiety detection in different user states;
[0031] FIG. 3B is a block diagram illustrating another example disclosed
system for anxiety detection, where the user states include user motion;
[0032] FIGS. 4A-4B show example equations for a multimodal Kalman
filter
for anxiety detection in different user states;
[0033] FIG. 5 is a block diagram of an example processing unit
implementing
an example system for anxiety detection;
[0034] FIG. 6 illustrates the experimental protocol for an example
study of
anxiety detection;
[0035] FIG. 7 is a chart representing the average heart rate across
all
participants in an example study of anxiety detection;
[0036] FIG. 8 is a chart representing the effect of the acceleration
smoothing
window length parameter on performance of an example anxiety detection system;
[0037] FIG. 9 is a chart representing the effect of the innovation
window
width parameter on performance of an example anxiety detection system;
[0038] FIG. 10 is a chart representing the effect of the detection
threshold
.. parameter on performance of an example motion detector in an example
anxiety
detection system;
[0039] FIG. 11 is a chart representing the effect of the RR smoothing
window
length parameter on performance of an example anxiety detector in an example
anxiety detection system;
CA 3033804 2019-02-14
- 7 -
[0040] FIG. 12 is a chart representing the effect of the innovation
window
length parameter on performance of an example anxiety detection system;
[0041] FIG. 13 is a chart representing the effect of the offset
parameter on
performance of an example anxiety detection system;
[0042] FIG. 14 is a chart representing the effect of the transition
probability
parameter on performance of an example anxiety detection system;
[0043] FIG. 15 is a chart representing the effect of the detection
threshold
parameter on performance of an example anxiety detector in an example anxiety
detection system; and
[0044] FIG. 16 illustrates an example operation of an example disclosed
anxiety detection system.
[0045] Similar reference numerals may have been used in different
figures to
denote similar components.
DESCRIPTION OF EXAMPLE EMBODIMENTS
[0046] The present disclosure describes examples for detection of
anxiety in
users with autism spectrum disorder (ASD), however it should be understood
that
the present disclosure is not limited to use in this population. For example,
the
present disclosure may be useful for detection of anxiety in any application
where it
may be useful or therapeutic to provide information, including feedback to the
user,
about the user's anxiety level. The present disclosure describes examples in
which
detection of anxiety is performed in different user states, such as a user
state
where there is user motion. It should be understood that the different user
states
that may be accommodated by the disclosed methods and systems are not limited
to user motion detected using motion sensors (e.g., accelerometers), and may
include other user states that may be detected using information from other
sensors.
[0047] To help in understanding the present disclosure, a brief
discussion of
the challenges in detection of anxiety is first provided. The autonomic
nervous
CA 3033804 2019-02-14
- 8 -
system (ANS) controls involuntary visceral functions of the body, including
cardiac
activity. The ANS is divided into parasympathetic and sympathetic pathways,
which
are associated with arousal and dampening of the autonomic responses,
respectively. These subsystems exert excitatory and inhibitory control over
the
heart muscle, and their combined effect can be observed through measurement of
heart rate.
[0048] States of anxiety are generally associated with sympathetic
dominance
and thus increased heart rate [19], [20]. This type of response, however, is
not
unique to anxiety. For example, physical activity is also associated with
sympathetic
dominance. This is the body's physiological response to meet the energy needs
of
the body resulting from muscle activity. To illustrate this, FIG. 1 shows the
effect of
physical activity (as indicated by acceleration - in this case represented as
unit-less
values that correlate directly with magnitude of acceleration) on heart rate.
As can
be seen from FIG. 1, heart rate is increased with increased motion.
[0049] This non-specificity of ANS arousal can lead to false positives and
decreased performance of anxiety detection systems in presence of user motion.
This is a limitation in naturalistic environments where users are often
mobile.
Although this challenge has not been directly addressed, a handful of studies
have
incorporated accelerometry signals in feature vectors used in supervised
classification for a stationary user [7], [8], [21]. Other studies have used
accelerometry signals to classify user activity. For example, a Kalman-like
state
estimator was proposed in [22] to detect four activity classes (sit, stand,
walk,
run). However, none of these studies have addressed how to use accelerometry
signals together with physiological signals to improve the specificity of
arousal
detection.
[0050] As well, the above-noted approaches use supervised learning
algorithms that require training data collected under different physical and
cognitive
states. This is typically cumbersome for users and impractical when a large
number
of movement situations are present.
[0051] An approach for unsupervised and real-time detection of anxiety is
described in [12] and in U.S. Patent No. 9,844,332, which uses a Kalman filter-
CA 3033804 2019-02-14
- 9 -
based approach for anxiety detection. FIG. 2 shows some example equations for
implementing this approach. The unsupervised approach eliminates the need for
cumbersome initial training as well as retraining to adapt to changing user
and
environmental conditions. However, this approach does not explicitly account
for
the effects of physical activity, or other user states that cause
physiological arousal
not specific to anxiety.
[0052] In the present disclosure, the Kalman filtering approach is
extended
into an interactive multiple model (IMM) filter (e.g., implemented using
modified
Kalman filters). The disclosed methods and systems provide unsupervised
anxiety
detection using multiple model filtering. In examples discussed below, heart
rate is
assumed to be the hidden state of a dynamical system that operates in one of
two
or more possible modes, each mode reflecting a different user state (e.g., a
rest
mode that assumes a lack of motion; and a motion mode that assumes the
presence of physical activity, and hence higher baseline heart rate). Each of
these
modes is associated with a respective modified Kalman filter. The IMM filter
includes all the modified Kalman filters for the different modes and also
combines
the state estimates from each modified Kalman filter together using mixing
probabilities.
[0053] Data received from sensors (including at least a physiological
sensor)
provides information for determining physiological arousal and for determining
the
user state. Baselines for each model are established, and deviations from
these
baseline models are used to change states appropriately and detect anxiety.
[0054] A simplified block diagram illustrating an example disclosed
anxiety
detection system 300 is shown in FIG. 3A. The anxiety detection system 300 may
be implemented using software, hardware, or a combination thereof. As will be
discussed further below, the anxiety detection system 300 may be implemented
by
or as part of another computing system or processing unit. The anxiety
detection
system 300 is configured to account for a plurality of defined user states.
The
occurrence of a particular user state is determined using user state
detector(s). An
IMM filter calculates a statistical prediction for anxiety, for all defined
user states.
An anxiety detector receives the calculated probabilities and the
determinations of
CA 3033804 2019-02-14
- 10 -
user states, and processes this information together to output an anxiety
indication
that accounts for the user being in one of the defined user states.
[0055] The system 300 receives as input data 302 received from one or
more
sensors. The input data 302 includes at least one set of physiological data
302a
(e.g., data received from a physiological sensor), which provides
physiological
information for determining arousal. The input data 302 also includes one or
more
sets of context data 302e-302k (e.g., data received from other sensors, which
may
or may not be physiological sensors), which provides information for
determining a
user state. In some example, context data 302e-302k may be obtained from a non-
sensor source, such as an external database or a software application. This
type of
context data 302e-302k may include, for example, data received from a calendar
application, a clock and/or a GPS application, among other possibilities. Such
context data 302e-302k may provide information about user state, such as
whether
the user is scheduled to be at a gym, whether the user is sleeping vs. awake,
or
whether the user is in a warm climate vs. cold climate.
[0056] It should be noted that, in some examples, contextual
information for
determining a user state may also be determined using data from a
physiological
sensor (e.g., a body temperature sensor may be used to determine a hot or cold
user state), thus there may be overlap between the type of data that is
considered
physiological data 302a and the type of data that is considered context data
302e-
302k. For simplicity, input data 302 may be used to generally refer to both
physiological data 302a and context data 302e-302k.
[0057] The input data 302 is processed by a feature extractor 304.
The
feature extractor 304 is used to process the raw input data 302 to extract
features
that can be used in state-space models by the user state detector(s) 308 and
the
IMM filter 306. The feature extractor 304 may, for example, process the raw
input
data 302 to remove noise or transitory signals. The feature extractor 304 may
also
quantify the raw input data 302 and/or label the raw input data 302 in a way
that
can be used in state-space models.
[0058] In some examples, the feature extractor 304 may perform different
processing on each input data 302, and may extract different features from
each
CA 3033804 2019-02-14
_
,
- 11 -
input data 302. For example, the feature extractor 304 may perform low-pass
filtering on input data from a temperature sensor to remove noise and
transitory
signals, based on the expectation that temperature changes are relatively
gradual.
On the other hand, input data from an accelerometer may be processed using a
smoothing window (e.g., as discussed in the example of FIG. 3B below) because
accelerometer data is expected to be more fast-changing. The feature extractor
304
may extract different features based on the different characteristics of
different
input data. For example, heart rate data contains unique physiological
characteristics, such as occurrence of the QRS complex, which can be used by
the
feature extractor 304 to quantify cardiac activity (e.g., as discussed in the
example
of FIG. 3B below). On the other hand, context data may be categorized by the
feature extractor 304 based on the user context indicated by the context data.
For
example, the feature extractor 304 may classify time data as being "day" or
"night". It should be understood that different ways of processing input data
and
extracting features may be used, within the scope of the present disclosure.
[0059] Although the present disclosure refers to feature(s) extracted
from the
input data, in some examples it may not be necessary to extract feature(s)
from
the input data 302, and the user state detector(s) 308 and/or IMM filter 306
may
process at least some of the input data 302 directly.
[0060] The output of the feature extractor 304 is received by one or more
user state detectors 308d-308n (generically referred to as user state detector
308).
Each user state detector 308 is configured to detect the occurrence of a
particular
user state, based on feature(s) of the input data 302. In some examples, each
user
state detector 308 may be implemented using a modified Kalman filter, and
determines a binary indicator for a particular user state based on one
extracted
feature.
[0061] In the present disclosure, the modified Kalman filter may be
based on
the algorithm shown in FIG. 2. The modified Kalman filter allows for
incorporation
of different states (e.g., baseline and motion, in the case of motion
detection),
unlike a traditional Kalman filter that assumes a single state (e.g., baseline
only).
In the modified Kalman filter, the baseline state model is updated using the
CA 3033804 2019-02-14
- 12 -
feature(s) of the input data 302 when the deviation from the baseline is not
significant (e.g., falling within a predicted noise model). When the
feature(s) of the
input data 302 deviates significantly from the baseline (e.g., falling outside
the
predicted noise model), this is considered to be indicative of the non-
baseline state
(e.g., motion state, in the case of motion detection) and the feature(s) of
the input
data 302 is not used to update the baseline state model. Instead, the output
is an
indicator of the non-baseline state.
[0062] Generally, the modified Kalman filter updates the baseline
state model
using a first weighting of the feature(s) when the feature(s) has a value
within a
predicted noise model, and updates the baseline state model using a lesser
second
weighting (which could be zero) of the feature(s) when the feature(s) has a
value
outside of the predicted noise model.
[0063] An example detailed implementation of the user state detector
308,
using a modified Kalman filter, is discussed below with respect to FIG. 3B
showing
an example embodiment for motion detection. In some examples, a user state
detector 308 may be implemented using other approaches aside from a modified
Kalman filter. For example, depending on the feature being analyzed by the
user
detector 308, the user detector 308 may determine occurrence of a particular
user
state by comparing the feature against a predefined threshold (e.g., a sleep
state is
determined if the time is later than a threshold time), or determining whether
the
feature fits into a particular category (e.g., a motion state is determined if
the
location is categorized as an exercise location), among other possibilities.
Each user
state detector 308 may use different approaches to determining the occurrence
of a
respective user state.
[0064] The IMM filter 306 is configured to implement a plurality of
modified
Kalman filters (each matched to a respective defined user state), to calculate
a
statistical prediction of anxiety in each possible user state. In this
example, the IMM
filter 306 further includes a model to mix the state estimates outputted from
the
plurality of modified Kalman filters, as discussed further below. The IMM
filter 306
accepts as input feature(s) extracted from the physiological data 302a, and
calculates a statistical prediction of anxiety for each possible user state.
An example
CA 3033804 2019-02-14
- 13 -
calculation of statistical prediction is the calculation of an innovation.
Generally, in
statistical analysis, the innovation is calculated as the difference between
an
observed value of a variable at a given time, and an optimal forecast value of
that
variable. The calculated innovation thus may be used as an indication of
whether
there is a deviation from baseline, to determine the presence of anxiety.
[0065] The IMM filter 306 addresses the problem of false positives,
discussed
above with respect to existing approaches for anxiety detection. In a prior
approach
that uses a modified Kalman filter (e.g., represented by the equations of FIG.
2), it
is assumed that the system follows a single linear-Gaussian model. This
assumption
limits the performance of the system in cases where changes in user state
(e.g.,
motion) may cause significant deviations from the baseline model. This results
in
growing filter error, and thus possible false anxiety detections. To mitigate
against
this challenge, the IMM filter 306 in the disclosed example system 300 uses a
jump-linear model to track the physiological features under different user
states,
and information from context data (e.g., acceleration data) is used to select
the
appropriate model for anxiety detection at any time point. An example detailed
implementation of the IMM filter 306 is discussed below with respect to FIG.
3B
showing an example embodiment.
[0066] The anxiety detector 310 receives, from the IMM filter 306,
the
calculated innovation for each possible user state, and applies weights to the
innovations using the determined user states outputted from the user state
detector(s). The weighted innovation is then used to calculate mean and
covariances for anxiety detection. An example detailed implementation of the
anxiety detector 310 is discussed below with respect to FIG. 3B showing an
example embodiment.
[0067] A detailed example implementation of the system 300 will be
discussed with reference to FIG. 3B, which shows an example embodiment of the
system 300 for detection of anxiety in the presence of possible user motion.
The
ability to accurately detect anxiety when the user is in a state of motion is
of
particular interest. Previous attempts at detection of anxiety have used
supervised
approaches, in which, using machine learning approaches (e.g., such as support
CA 3033804 2019-02-14
- 14 -
vector machine, K-nearest neighbour, and decision tree algorithms) models have
been trained to detect arousal of the ANS based on cardiac activity and other
physiological signals [8], [12], [30]-[32]. However, most of these algorithms
have
been evaluated based on data collected while the subject is at rest. The
presence of
user motion challenges the operation of these systems as physical activity is
also
associated with ANS arousal. This can result in false positives and
performance
degradation. In the present disclosure, an example of the anxiety detection
system
300 is described below, in which the disclosed unsupervised approach to
anxiety
detection is used to account for ANS changes related to physical activity.
[0068] It is well-known that cardiac activity increases during states of
ANS
arousal associated with both physical activity and anxiety. However, it has
not been
clearly established clear if physical activity could give rise to non-anxiety-
specific
arousal that could be falsely detected as anxiety. It also has not been
clearly
established if there can be detectable anxiety-related increase in arousal
during
physical activity. In an example study, discussed further below, it has been
found
that heart rate does in fact increase significantly in response to anxiety
tasks, even
in presence of physical activity. This further motivates the need for anxiety
detection methods that can accurately detect anxiety even in presence of user
motion.
[0069] In the example of FIG. 38, it is assumed that the system 300 can
operate in one of two user states: motion or no motion. An accelerometry
signal
(e.g., from a tri-axial accelerometer) is used to modulate how the IMM
switches
between states and to determine the threshold for anxiety detection in each
mode.
In this example, the system 300 enables reduction of false anxiety detections,
by
accounting for physical activity-related arousal. Further, the system 300
enables
detection of anxiety-related arousal during physical activity.
[0070] In the example of FIG. 3B, the input data 302 includes
electrocardiogram (ECG) data 302b (as an example of physiological data 302a),
and tri-axial accelerometer data 302c (as an example of context data 302b).
The
input data 302 are processed through the feature extractor 304 to obtain the
heart
rate and accelerometry feature time series, which are then processed by a
motion
CA 3033804 2019-02-14
- 15 -
detector 308a (as an example of user state detector 308) and the IMM filter
306.
The anxiety detector 310 receives output from the IMM filter 306 and the
motion
detector 308a to produce the anxiety indication 312 (e.g., a binary value).
[0071] In the example of FIG. 3B, the feature extractor 304 processes
the
.. accelerometer data 302c and the ECG data 302b as follows. The smoothed
moving
standard deviation of the accelerometer data 302c is used to compute the
acceleration vector o-kas follows:
k-FtvA
1
E
2wA+1
(1)
[0072] where k is the time index and is the mean of viover the window
of
interest. The signal v,is the magnitude of the acceleration data in the x, y,
z
directions at time i (e.g., bandpass filtered between 0.25 and 5Hz, and re-
sampled
to 5Hz). The window length WA may be selected based on experimental or
empirical
testing.
[0073] The ECG data 302b is quantified based on the length of RR
intervals,
for example extracted using the Pan-Tompkins algorithm [23] and re-sampled
uniformly at 5Hz. The RR time-series is used to compute a slowly varying trend
zkat
time k defined as [12]:
Zk = _________ + RR;
witil 1
(2)
[0074] where the window size wRR is the smoothing window length,
which may
be determined using experimental or empirical testing. The results of the
processing by the feature extractor 304 are then provided for further
processing by
the IMM filter 306 and the motion detector 308a as described below.
[0075] The motion detector 308a processes the accelerometry feature
time
series, 0-k, to produce a binary indicator /kmotion (where 0 indicates no
motion, 1
indicates motion). The motion detector 308a may be implemented using any
CA 3033804 2019-02-14
- 16 -
suitable algorithm. For example, a modified Kalman filter may be used with the
following state-space model:
Xk = Xk-1+ Wk (3)
Crk= Xk+ Vk (4)
[0076] where xkis a state variable modelling the evolution of user motion,
crk is
acceleration vector defined in Equation 1, and wkand vkare zero-mean Gaussian
system and measurement noises, respectively, determined as in [12]. Other
approaches may be used for implementing the motion detector 308a, including
supervised methods (e.g., using machine learning algorithms) or other
unsupervised methods.
[0077] As noted above, the IMM filter 306 in this example uses a jump-
linear
model to track the RR-series under rest and motion conditions, and makes use
of
accelerometer data to select the appropriate model for anxiety detection at
any
time point.
[0078] A jump-linear model is defined with two modes ME frest,motionl. In
particular, it is assumed that mode switching (mode jump process) is a Markov
process with transition probabilities defined a priori as:
py P(Mk = = (5)
For simplicity, the transition matrix is modeled as:
-
P.¨ P
\j
- 1)
17 (6)
[0079] To track the state, the IMM filter 306 uses a filter bank
comprised of
two modified Kalman filters, each matched to rest or motion modes,
respectively.
Each of these filters assumes a linear-Gaussian state-space model defined
below:
m m
x,. --= _ (7)
.111 .111
zic tk (8)
where the state estimate xmkis the "ideal" slow varying RR trend at time k for
model
ME frest,motionl, and zkmis the observed RR trend defined in Equation 2. The
process
CA 3033804 2019-02-14
- 17 -
noise wkmand measurement noise vkmare assumed to be independent, zero-mean
Gaussian noise with variances Qmkand Rkm, respectively. Each mode-matched
filter
tracks the baseline RR series under the assumption of no anxiety, allowing the
anxiety detection under both rest and motion states. The initial condition for
the
rest-matched filter is computed from the data, while the initial condition for
the
motion filter is assumed to be the rest state plus an offset (the offset may
be
selected experimentally, as discussed further below).
[0080] The estimates from each filter are computed following the
approach of
the IMM filter [24]. This approach is based on combining state estimates and
covariances from each filter using estimated model probabilities. These mixing
probabilities may be computed using the following equation:
MU M
MI() P'
it
k¨ilk-1 ,õmu õM
[0081] k (9)
[0082] These probabilities are used to compute the mixed initial
conditions for
each filter using the filter's estimate from the previous iteration, according
to the
equation:
om (JIM
xk-11k¨I (10)
DOM t. i õ 1 0
214 1 -1-
(17 OM 2) (11)
1 Ek_lik -
where U c frest,motionl. Based on the estimates and their covariances, the
prediction
that contributes to the innovation and its covariance STI is computed for
each
model, as follows:
,m Al
`== k k k 1 k ¨1 (12)
=__ irolusal Rk (1 rroausal)AT D
k ¨1 Iv 114C = (13)
[0083] Finally, to prepare for the next iteration of the filter, the
probability of
each mode being correct, pmk, is estimated using each filter's likelihood
function, as
follows:
CA 3033804 2019-02-14
- 18 -
.)V(e.1, Sr )(EuJi'i puM/41. )
õiv/
Pkik
(14)
[0084] The innovation signal l quantifies the amount of deviation
between
the observation and the mode-matched baseline. The output, Ikmatian of the
motion
detector 308a is used to choose the innovation that will be used to determine
the
presence of arousal. In particular,
ck = (1, ii,"" ")fr rk notion ert,totion
(15)
[0085] The innovation ckis then used by the anxiety detector 310 to
compute
mean and covariances for anxiety detection as in [14
1 (16)
gic = N-1-1 Ei
i= 0
1
ez
Wm 4-1 (17)
i=k
k k
¨ (6. ¨ 4)2,
(18)
i=o
where Wnis a moving average window. The arousal indicator Ikamusal is
determined
using the following equation:
Tarou.s al
1 if ¨ 14 >
k 0 otherwise.
(19)
[0086] The anxiety detector 310 outputs the arousal indicator
Ikamusal as the
anxiety indication 312.
[0087] FIGS. 4A-4B show equations summarizing the disclosed example
system 300, in the case where user motion is taken into account. In
particular, the
CA 3033804 2019-02-14
- 19 -
equations of FIGS. 4A-4B may be used to implement the IMM filter 306 with a
plurality of modified Kalman filters (rather than a conventional IMM filter
using
regular Kalman filters). It should also be noted that a conventional IMM
filter does
not include thresholding, unlike the present disclosure. The example
.. implementation disclosed herein computes the innovation as a combination of
innovations from the plurality of modified Kalman filters, with the weights
depending on the user state. One skilled in the art would understand that the
example equations of FIGS. 4A-4B are exemplary and are not intended to be
limiting. For example, the equations shown in FIGS. 4A-4B may be adapted to
take
into account other user states and/or other physiological information, for
example
by adding state-space equations, calculating the relevant innovations and
including
the appropriate additional terms in the thresholding.
[0088] It should be noted that although the example of FIG. 3B
accounts for
motion as a binary state (e.g., motion vs. no motion), in other examples the
system 300 may be adapted to account for different degrees of motion (e.g.,
running vs. walking vs. no motion) and to account for the user being in a
vehicle
(e.g., to avoid misclassifying acceleration while the user is in a car as
being user
motion), among other possible modifications. For example, in order to account
for
different degrees of motion, there may be a plurality of motion detectors
308a,
.. each of which is adapted to detect occurrence of user motion at a different
threshold. The IMM filter 306 may then be adapted to include modified Kalman
filters for each of the motion thresholds. It should also be noted that
although the
example of FIGS. 3A and 3B provide a binary anxiety indicator (e.g., anxiety
detected or no anxiety detected), in other examples the output of the anxiety
detection system 300 may be non-binary (e.g., may include multiple levels of
anxiety detection at different thresholds such as no anxiety, mild anxiety,
moderate
anxiety and high anxiety; or may provide anxiety detection along a continuous
or
analog scale).
[0089] Although the present disclosure describes user motion as an
example
user state that can give rise to non-anxiety-specific physiological arousal,
it should
be understood that other user states may similarly affect the accuracy of
anxiety
CA 3033804 2019-02-14
- 20 -
detection. The present disclosure may be adapted to account for such other
user
states, for example using context data from different physiological and/or non-
physiological sensors, and using different user state detectors (e.g., as
described
with reference to FIG. 3A).
[0090] FIG. 5 is a simplified block diagram of an example processing unit
500
in which the disclosed system 300 (e.g., as shown in FIGS. 3A and 3B, and
represented by the equations of FIGS. 4A-4B) may be implemented. Although FIG.
5 shows a single instance of each component, there may be multiple instances
of
each component in the processing unit 500.
[0091] The processing unit 500 may be any suitable computing device, such
as a portable electronic device, which may be a handheld electronic device
(e.g., a
mobile phone, a smartphone, a tablet) or a wearable electronic device (e.g.,
computerized eyeglasses or computerized wrist devices). Such a device may be
carried or worn by the subject during daily activities and may thus be able to
provide real-time monitoring of the anxiety level of the subject, as well as
being
able to provide real-time feedback to the subject and/or clinician about the
arousal
state of the subject. In some examples, the present disclosure may be
implemented
in conventional portable electronic devices and using conventional
physiological
sensors. In some examples, some or all computer-executable instructions for
implementing the system 300 may be stored externally from the electronic
device
(e.g., in an external centralized server, in a distributed network, or
accessible via
cloud computing).
[0092] In some examples, the processing unit 500 may include any
suitable
off-the-shelf wearable device with built-in physiological sensor (e.g., a
wearable
activity tracker) and any suitable consumer portable electronic device. A
downloadable software application (also referred to as an app) for
implementing the
disclosed method may be installed onto the processing unit 500. The software
may
be updated as appropriate to incorporate new relaxation techniques, different
numbers and/or types of physiological sensors, and/or new feedback techniques,
for example.
CA 3033804 2019-02-14
- 21 -
[0093] The processing unit 500 may include one or more processing
devices
502, such as a processor, a microprocessor, an application-specific integrated
circuit (ASIC), a field-programmable gate array (FPGA), a dedicated logic
circuitry,
or combinations thereof. The processing unit 500 may include one or more
output
devices 504 (e.g., a display, a speaker, a tactile/vibration mechanism and/or
a
light), which may provide feedback (e.g., to the subject and/or clinician)
based on
the detected anxiety level. The processing unit 500 may optionally include one
or
more input devices 506 (e.g., a keyboard, a mouse, a microphone, a
touchscreen,
and/or a keypad), which may receive input (e.g., command instructions) from a
user. Although not shown, in some examples the processing unit 500 may include
components (e.g., network interfaces) to enable wired or wireless
communication.
[0094] The processing unit 500 may also include one or more storage
units
508, which may include a mass storage unit such as a solid state drive, a hard
disk
drive, a magnetic disk drive and/or an optical disk drive. The processing unit
500
may include one or more memories 510, which may include a volatile or non-
volatile memory (e.g., a flash memory, a random access memory (RAM), and/or a
read-only memory (ROM)). The non-transitory memory(ies) 510 may store
instructions for execution by the processing device(s) 502, such as to
implement an
example of the disclosed anxiety detection system 300. The memory(ies) 510 may
also store databases of relaxation techniques, a subject's history of anxiety,
a log
of occurrences of detected anxiety, and other information about the subject,
as well
as patterns of physiological activity in the subject or larger populations,
for
example.
[0095] The memory(ies) 510 may include other software instructions,
such as
for implementing an operating system and other applications/functions. In some
examples, one or more data sets and/or modules may be provided by an external
memory (e.g., an external drive in wired or wireless communication with the
processing unit 500) or may be provided by a transitory or non-transitory
computer-readable medium. Examples of non-transitory computer readable media
include a RAM, a ROM, an erasable programmable ROM (EPROM), an electrically
erasable programmable ROM (EEPROM), a flash memory, a CD-ROM, or other
portable memory storage. Some or all of the instructions and/or data described
CA 3033804 2019-02-14
- 22 -
above as being stored in the memory(ies) 510 and/or storage unit(s) 508 may be
stored externally (e.g., in an external centralized server, in a distributed
network,
or accessible via cloud computing) and accessible by the processing unit 500.
[0096] The processing unit 500 in this example includes a sensor
subsystem
512, which includes one or more sensor units, in this example a heart rate
sensor
514, an accelerometer 516 and one or more other sensors 518 (generically
referred
to as the sensor subsystem 512). The sensor subsystem 512 may include sensors
positionable on or near the subject for obtaining physiological data. Other
sensors
for obtaining context data may be positional on or near the subject, or may
not
need to be close to the subject. Any suitable sensor(s) may be used, such as
wearable heart rate sensors or electrodes. Although shown as being part of the
processing unit 500, in some examples one or more sensors of the sensor
subsystem 512 may be external to the processing unit 500 and may communicate
data via signals (e.g., wired or wireless signals) to the processing device
502.
[0097] There may be a bus 520 providing communication among components
of the processing unit 500, including the processing device(s) 502, output
device(s)
504, optional input device(s) 506, storage unit(s) 508, memory(ies) 510 and
the
sensor subsystem 512. The bus 520 may be any suitable bus architecture
including,
for example, a memory bus, a peripheral bus or a video bus.
[0098] The example processing unit 500 may provide feedback to the subject
and/or a clinician about the arousal state of the subject, via the output
device 504.
The anxiety indication 312 outputted by the anxiety detection system 300 may
be
used to determine the type of output to be provided. For example, when the
anxiety indication 312 indicates that the subject is at or close to
experiencing
anxiety, the processing device 502 may cause the output device 504 to provide
visual and/or audio feedback to indicate the subject is experiencing anxiety
and/or
to enable relaxation or desensitization.
[0099] The type of output that is provided, based on the anxiety
indication
312, is not limited. For example, the output may include audio output, tactile
output, visual output, or a combination of these. The output may be directed
to the
clinician, in which case the output may simply provide information about the
CA 3033804 2019-02-14
- 23 -
presence or absence of anxiety. Additionally or alternatively, the output may
be
directed to the subject and/or care-giver, in which case the output may be
designed
to help the subject return to a state of lessened or no anxiety. Such further
output
may be in the form of visual or audio suggestions of relaxation techniques, or
distractions (all of which may be pre-stored in the memory(ies) 510), for
example.
The subject's anxiety level may be monitored (using the output from the
anxiety
detection system 300) while this output is provided, so that the success of
the
relaxation/distraction technique may be determined.
[00100] In some examples, a log of the subject's anxiety state may be
created
and the log may be stored in the memory 510 and/or outputted to be stored in
an
external memory. Information in the log may include the subject's anxiety
level (as
represented by the anxiety indicator) and the associated context, for example.
Information included in such a log may be useful to help the subject and/or
clinician
to identify anxiety triggers and successful relaxation techniques, for
example.
[00101] In some examples, information about the subject's anxiety state may
be used as a measurement or representation of the engagement of the subject in
an activity (e.g., user engagement in a game). The anxiety indication 312 may
be
used as feedback for automatic, semi-automatic or manual adjustment of the
activity (e.g., increasing or decreasing difficulty of the game) in order to
increase or
decrease user engagement, for example.
Example study
[00102] An example of the disclosed system 300 was evaluated in a
study
using data collected from a sample of children and youth with a diagnosis of
ASD
(n=15). All participants had a clinical diagnosis of ASD, supported by the
gold-
standard instruments namely, the autism diagnostic observation schedule (ADOS)
and the autism diagnostic interview - revised (ADI-R). Participants were
between
the ages of 8 and 16 and had full-scale IQ scores greater than 50.
Participants
using beta-blockers were excluded from the study as these medications have a
significant effect on physiological arousal. Participants' IQ was assessed
using the
Wechsler Abbreviated Scale of Intelligence (WASI), and ASD symptom severity
was
CA 3033804 2019-02-14
- 24 -
characterized using the Social Communication Questionnaire (SCQ). Table I
details
the characteristics of the sample:
Table I
Measure Mean (SD)
Age 14 (1.77)
Sex (Male:Female) 9 : 6
Full-scale IQ 89.9 (15.40)
SCQ scoare 20 (7.37)
[00103] The ShimmerTM 2r sensor from Shimmer technologies was used to
collect physiological and motion data. The sensor consisted of an ECG
acquisition
system, accelerometer, and wireless bluetooth capabilities that allowed for
untethered, wireless communication to a data collection computer. Gel-
electrodes
where attached to four loci on the chest: the right and left arm electrodes
placed in
the first intercostal space and on the midclavicular line, and the right and
left leg
electrodes placed on the midclavicular line inferior to the tenth rib. The
modified
four-electrode placement was used to reduce the denigrative effect of motion
artefacts on the signal-to-noise ratio of the ECG signal. The Shimmer 2r was
attached to the chest to allow measurement of changes in torso acceleration
along
the x, y, and z planes using the on-board accelerometer. The accelerometer and
ECG signals were sampled at 250Hz.
[00104] Experimental Protocol
[00105] As shown in FIG. 6, the testing session consisted of three
stages
during which the participants were asked to either stand, slow walk, or walk a
comfortable speed (fast walk) on a treadmill. Prior to the start of each
stage, a
resting baseline was captured while the participant was seated, and engaged in
a 5-
minute movie clip. The first resting phase was used to initialize system
parameters.
The treadmill was then set to speeds appropriate to the activity level being
tested:
CA 3033804 2019-02-14
- 25 -
during the standing stage, the treadmill was not turned on, slow walking was
set to
the first speed setting supported by the treadmill, and during the fast
walking, the
treadmill was set to a comfortable speed that aligned with the participant's
gait.
[00106] During each of the stages, participants completed a baseline
and
stressor activity. The baseline phase consisted of watching a 5-minute clip
from
BBCs Planet Earth 2. Clips were chosen specifically to not include scenes that
can
induce anxiety, and rapid changes in music, violence, or frightening scenes
were
excluded from any of the clips shown to the participants. The Stroop Colour-
Word
Interference test was chosen as the stressor activity to induce anxiety-
related
arousal. This task has previously been used to induce anxiety in many studies
[12],
[25]-[28]. During this test, participants were asked to name the font colour
of the
word that is being displayed on the screen. The words were chosen at random,
as
are the font colour, from a list of colours: blue, red, green, purple, and
yellow. The
Stroop tests were five minutes in length, and were divided into five, one-
minute
blocks. Blocks 1, 3 and 5 were set to present words at two second intervals,
and
blocks 2 and 3 at 1.25-second intervals. The congruent section (matching
colour
name and print colour) were made up of blocks 1, and 5, while the rest of the
blocks were made up of the in-congruent section (conflicting colour name and
print
colour). This protocol has been previously used in studies eliciting anxiety
responses in children with ASD [12].
[00107] Sensitivity, specificity, and accuracy were to evaluate the
performance
of the filter in classifying baseline and condition states. Each of the
metrics were
defined as:
TP
Sensitivity =
TP + FN'
TN
SileCificity =
TN + FP'
St11.9itivity + Specificity
Accuracy 7--
2
[00108] where true positive TP, true negative TN, false positive FP, and
false
negative FN are calculated relative to ground truth signals for motion and
arousal.
CA 3033804 2019-02-14
- 26 -
The ground truth for motion detection was determined to be motion when the
participant walked and no motion otherwise.
[00109] For arousal, determining the ground truth is challenging due
to
difficulties in obtaining reliable self- or parent reports to gauge the
emotional states
of children with ASD. Therefore, the approach of [12] was followed, and
intervals
with increases of two or more beats in heart rate relative to the preceding
baseline
mean were designated as arousal intervals.
[00110] The heart rate response to the motion and anxiety tasks were
characterized. In addition, the performance of the disclosed anxiety detection
system 300 as well as its sensitivity to its parameters is evaluated.
- [00111] Characterisation of Heart Rate Response
[00112] The average heart rate across all participants is presented in
FIG. 7 for
all study tasks. In FIG. 7, BL indicates when the participants are performing
baseline task, and Stroop indicates the color-word interference task. Bars
represent
standard error. The effect of task on heart rate was analysed using repeated
measures linear regression analysis. In particular, heart rate differences
between
the motion and arousal conditions were analyzed. Based on Bonferroni
correction
for six comparisons, a significance level of 0.01 was used.
[00113] Effect of motion on heart rate: The analyses showed
significantly
increased heart rate during the fast walking baseline compared to slow walking
and
standing baselines (fast walking - standing: estimated difference=11.89 2.03
beats/min, p <0.0001; fast walking - slow walking: estimated difference=7.14
1.61
beats/min, p <0.0001).The difference in heart rate between standing and slow
walking baselines was also significant (slow walking - standing: estimated
difference=4.75 1.79 beats/min, p= 0.01).
[00114] Effect of anxiety on heart rate: There was a significant
increase in
heart rate during the Stroop task compared to the baseline for all three
motion
conditions (standing - baseline: estimated difference=3.79 1.29 beats/min,
p=
0.004; slow walking - baseline: estimated difference=4.62 1.17 beats/min, p=
0.0001; standing - baseline: estimated difference=6.05 2.08 beats/min, p =
0.004).
[00115] Motion Detection
CA 3033804 2019-02-14
- 27 -
[00116] The effect of system parameters on the performance of the
motion
detector 308a was examined. In particular, the effect of the width of the
acceleration feature smoothing window, innovation window width, and the
detection
threshold on sensitivity, specificity, and accuracy were examined.
[00117] Acceleration smoothing window length: The parameter WA 15 used to
compute the acceleration feature (Equation 1). FIG. 8 depicts the effect of
this
parameter on algorithm performance. As seen, algorithm performance in this
instance was found to be optimized for WA= 5.
[00118] Innovation window width: The window width It; is used to
smooth the
innovation time-series used for thresholding. The effect of this parameter on
system performance is shown in FIG. 9. As seen, in this instance, the value of
= 50 was found to maximize performance of the motion detector 308a.
[00119] Detection threshold: The threshold TA is used in the motion
detector
308a. FIG. 10 shows the effect of this parameter on motion detection
performance
and, in this instance, a value of TA= 0 was found to be preferrable.
[00120] Anxiety Detection
[00121] RR smoothing window length: A moving average window of length
WRR
was used to compute the slowly varying RR trend for the anxiety detector 310.
FIG.
11 depicts the effect of this parameter on filter performance and in this
instance a
value of WRR= 50 was found to provide optimal performance.
[00122] Innovation Window length: FIG. 12 shows the effect of Mon
performance. This parameter is the innovation smoothing window length. In this
instance, the value of vvõ,-- 50 was found to provide the best performance on
this
example dataset.
[00123] Offset: The offset parameter specifies the difference in initial
state
means between the filers matched to rest and motion. FIG. 13 shows the effect
of
this parameter on algorithm performance. The figure suggests that the anxiety
detector 310 is not highly sensitive to the initialization offset. An offset
of 10
beats/minute is chosen for the remaining analyses.
[00124] Transition probabilities: The effect of transition probabilities on
algorithm performance was examined (FIG. 14). These values impact the
CA 3033804 2019-02-14
- 28 -
computation of mode probabilities in the algorithm (motion versus rest). As
seen, in
this instance, optimal algorithm performance was found to be achieved with a
relatively wide range of parameter values between 0.5 and 0.9.
[00125] Detection threshold: FIG. 15 depicts the effect of threshold
rõõõon the
sensitivity, specificity and accuracy of the anxiety detector 310, suggesting
that the
best results, in this instance, are obtained with for Tanx= 0.5.
[00126] FIG. 16 provides an example illustrating an example operation
of the
disclosed anxiety detection system. In particular, the internal signal for
detected
user state (1= user motion state; 0=no motion state), the internal
thresholding
signal, and outputted anxiety indication signal (1=anxiety-related arousal
detected;
0=anxiety-related arousal not detected) are shown. In this example, anxiety-
related arousal is detected when the thresholding signal exceeds 0.5.
[00127] In various examples, the present disclosure describes anxiety
detection methods and systems, which can detect anxiety with accuracy in
different
user states including states (e.g., user motion) that may cause physiological
arousal that is not specific to anxiety. In particular, the disclosed methods
and
systems provide an unsupervised algorithm for anxiety detection. Another
unsupervised approach, using a modified Kalman filter, has been described in
U.S.
Patent No. 9,844,332, having a common inventor to the present application.
[00128] Table II compares the performance of an example of that previous
algorithm to an example of the presently disclosed method. Parameters of both
algorithms were optimized to obtain the best accuracy. As seen, the presently
disclosed method provides a significant advantage in terms of achieved
accuracy,
and especially with regards to improving algorithm specificity. Performance
was
compared under subject conditions of standing still (SS), slow walking (SW)
and
fast walking (FW). The performance averaged over all conditions was also
compared. In particular, 16% and 22% improvement in specificity is achieved by
the presently disclosed method for the slow walking and fast walking
conditions,
respectively.
CA 3033804 2019-02-14
- 29 -
TABLE II
Approach Condition Accuracy Sensitivity Specificity
Modified SS 0.82 0.74 0.89
Kalman SW 0.82 0.85 0.79
filter FW 0.87 0.97 0.77
[12] All 0.84 0.82 0.85
Example SS 0.87 0.78 0.95
disclosed SW 0.93 0.90 0.95
method FW 0.99 0.99 0.99
All 0.91 0.85 0.97
[00129] These results demonstrate that the example disclosed method is
able
to detect anxiety responses to an anxiety task (Stroop task) with accuracy
greater
than 85% during three motion scenarios: standing still, slow walking, and fast
walking. This represents a significant improvement compared to the state-of-
the-
art anxiety detection systems, especially with regards to specificity of
anxiety
detection.
[00130] The disclosed methods and systems thus may be used to provide
objective feedback indicating anxiety level, which may be useful for
populations
who have difficulties with self-awareness and communication of these states,
such
as children with a diagnosis of ASD. Other populations may also benefit from
examples disclosed herein. The disclosed methods and systems may be
implemented in consumer devices (e.g., wearable activity monitors), may be
used
.. for general wellness monitoring, may be used in a clinical setting (e.g.,
for
treatment of anxiety or desensitization treatments), or other such
applications.
Further, the disclosed methods and systems may be adapted for other user
states
(e.g., hot/cold, sleeping/awake, etc.), which may help to enable accurate
anxiety
detection in everyday situations and naturalistic settings.
[00131] The disclosed methods and systems may be integrated into a larger
system, such as a virtual reality platform, for anxiety treatment and/or
desensitization, for example.
CA 3033804 2019-02-14
- 30 -
[00132] In the example study discussed above, the performance of the
example disclosed method was examined with respect to variations in certain
parameters. While certain parameter values (e.g., smoothing window lengths)
were
found to be optimal in this instance, it should be understood that they are
.. exemplary and are not intended to be limiting. Further, it should be
understood that
although certain parameters were examined in the case were user motion is of
concern, other parameters may be relevant for other user states. One skilled
in the
art would understand how to select and adjust parameters, for example using
routine trial-and-error or through empirical methods.
[00133] The disclosed methods and systems use a modular approach where
additional filter models and user state detectors can be added to integrate
other
user states that may be associated with ANS arousal. The disclosed methods and
systems use an unsupervised approach for anxiety detection, which may avoid
the
expense and training required for supervised learning approaches.
[00134] In an example disclosed herein, an anxiety detection system is
provided in which user motion is taken into account, to enable accurate
detection of
anxiety both when the user is in motion and when there is no user motion. The
example anxiety detection system combines information from an accelerometer
with information from a heart rate monitor, resulting in a system that is
resilient
against motion, and avoids false positives.
[00135] The disclosed methods and systems may be implemented in
wearable
devices that can provide a real-time and objective feedback to a subject
and/or a
clinician about the subject's arousal state. In particular, the disclosed
methods and
systems may enable anxiety detection in different user states, such as user
motion,
which may facilitate the translation of the technology from laboratory
environments
to everyday settings.
[00136] In some examples, the present disclosure may be embodied in
the
form of instructions accessible by an electronic device via cloud computing.
In some
examples, the present disclosure may be embodied in the form of an application
programming interface (API) (e.g., at a server) accessible by an electronic
device.
CA 3033804 2019-02-14
- 31 -
[00137] Although the present disclosure is described, at least in part,
in terms
of methods, a person of ordinary skill in the art will understand that the
present
disclosure is also directed to the various components for performing at least
some
of the aspects and features of the described methods, be it by way of hardware
components, software or any combination of the two. Accordingly, the technical
solution of the present disclosure may be embodied in the form of a software
product. A suitable software product may be stored in a pre-recorded storage
device or other similar non-volatile or non-transitory computer readable
medium,
including DVDs, CD-ROMs, USB flash disk, a removable hard disk, or other
storage
media, for example. The software product includes instructions tangibly stored
thereon that enable a processing device (e.g., a personal computer, a server,
or a
network device) to execute examples of the methods disclosed herein.
[00138] The present disclosure may be embodied in other specific forms
without departing from the subject matter of the claims. The described example
embodiments are to be considered in all respects as being only illustrative
and not
restrictive. Selected features from one or more of the above-described
embodiments may be combined to create alternative embodiments not explicitly
described, features suitable for such combinations being understood within the
scope of this disclosure.
[001.39] All values and sub-ranges within disclosed ranges are also
disclosed.
Also, although the systems, devices and processes disclosed and shown herein
may
comprise a specific number of elements/components, the systems, devices and
assemblies could be modified to include additional or fewer of such
elements/components. For example, although any of the elements/components
disclosed may be referenced as being singular, the embodiments disclosed
herein
could be modified to include a plurality of such elements/components. The
subject
matter described herein intends to cover and embrace all suitable changes in
technology.
CA 3033804 2019-02-14
