U.S. patent application number 17/360403 was filed with the patent office on 2021-10-21 for method and apparatus for deriving a mental state of a subject.
This patent application is currently assigned to Nitto Denko Corporation. The applicant listed for this patent is Nitto Denko Corporation. Invention is credited to Aye Aung, Kittipong Kasamsook, Pimporn Muaynoi, Visit Thaveeprungsriporn.
Application Number | 20210321924 17/360403 |
Document ID | / |
Family ID | 1000005682056 |
Filed Date | 2021-10-21 |
United States Patent
Application |
20210321924 |
Kind Code |
A1 |
Aung; Aye ; et al. |
October 21, 2021 |
Method And Apparatus For Deriving A Mental State Of A Subject
Abstract
A method (1500) for deriving a mental state of a subject is
disclosed. The method comprises receiving (1502) a bio-signal from
the subject; calculating respective statistical variations of at
least two physiological parameters derived from the bio-signal;
determining (1504) an arousal level of the subject based on the
calculated statistical variations of the at least two physiological
parameters; deriving a time-domain heart rate variability signal
from the bio-signal for calculating at least two heart rate
variability parameters; determining (1506) a valence level of the
subject based on the at least two heart rate variability
parameters; and deriving (1508) the mental state from the arousal
level and valence level. A related apparatus is also disclosed.
Inventors: |
Aung; Aye; (Singapore,
SG) ; Kasamsook; Kittipong; (Singapore, SG) ;
Thaveeprungsriporn; Visit; (Singapore, SG) ; Muaynoi;
Pimporn; (Singapore, SG) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Nitto Denko Corporation |
Osaka |
|
JP |
|
|
Assignee: |
Nitto Denko Corporation
Osaka
JP
|
Family ID: |
1000005682056 |
Appl. No.: |
17/360403 |
Filed: |
June 28, 2021 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
15537967 |
Jun 20, 2017 |
11076788 |
|
|
PCT/SG2015/050500 |
Dec 23, 2015 |
|
|
|
17360403 |
|
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/16 20130101; A61B
5/024 20130101; A61B 5/0816 20130101; A61B 5/14507 20130101; A61B
5/021 20130101; A61B 5/0205 20130101; A61B 5/165 20130101; A61B
5/7239 20130101; A61B 5/02405 20130101 |
International
Class: |
A61B 5/16 20060101
A61B005/16; A61B 5/0205 20060101 A61B005/0205; A61B 5/021 20060101
A61B005/021; A61B 5/024 20060101 A61B005/024; A61B 5/08 20060101
A61B005/08; A61B 5/00 20060101 A61B005/00 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 30, 2014 |
SG |
PCT/SG2014/000622 |
Claims
1. A non-transitory computer-readable medium storing a set of
instructions, which when executed by a processor of a wearable
device, cause the processor to provide emotional conditioning, the
set of instructions being executable to: obtain measurements of
physiological parameters related to a first user; track the first
user emotional state by analyzing the measurements of the
physiological parameters from the first user over a desired time
period; continuously monitor whether any of the measurements of the
physiological parameters of the first user has changed; generate a
set of chart associated with the desired time period that is
representative of the measurements of the physiological parameters;
cause the wearable device to send the set of chart to a
telecommunication device associated with a second user; receive,
from the second user, an indication of which of the physiological
parameters is to be altered by the first user to assist with
managing stress.
2. The non-transitory computer-readable medium storing a set of
instructions according to claim 1, wherein the physiological
parameters comprises a standard deviation of pulse pressure.
3. The non-transitory computer-readable medium storing a set of
instructions according to claim 1, wherein the indication includes
a resting, mental stress test and/or recovery.
4. The non-transitory computer-readable medium storing a set of
instructions according to claim 1, wherein the set of charts having
the resting, mental stress test and/or recovery.
5. The non-transitory computer-readable medium storing a set of
instructions according to claim 4, wherein the set of charts
further includes a trend displayable on a display unit.
6. The non-transitory computer-readable medium storing a set of
instructions according to claim 5, wherein the set of charts
further includes a tracking of data over a time period
representative of the first user.
7. The non-transitory computer-readable medium storing a set of
instructions according to claim 6, wherein the tracking of data
presented to the first and second user in textual or graphical
format.
8. The non-transitory computer-readable medium storing a set of
instructions according to claim 1, wherein the wearable device
further includes an audio output module.
9. The non-transitory computer-readable medium storing a set of
instructions according to claim 8, wherein the audio output module
providing instructions to the first user a set of breathing
instructions reflecting the indication.
10. The non-transitory computer-readable medium storing a set of
instructions according to claim 1, wherein the wearable device and
telecommunication device configured to provide audio and visual
stimulations to assist the first user to relax.
11. The non-transitory computer-readable medium storing a set of
instructions according to claim 10, wherein the wearable device and
telecommunication device configured to generate an alert to the
first user for prolonged stress exposure.
12. The non-transitory computer-readable medium storing a set of
instructions according to claim 1, wherein the second user tracking
the first user emotion state over a period of time representative
of an activity and sleep period.
13. The non-transitory computer-readable medium storing a set of
instructions according to claim 12, wherein the second user
reviewing the first user emotion state over the period of time to
diagnose and provide an effective treatment.
14. The non-transitory computer-readable medium storing a set of
instructions according to claim 13, wherein the period of time is
configurable.
15. A method of providing emotional conditioning of a subject,
comprising: obtaining measurements of physiological parameters
related to a first user; tracking the first user emotional state by
analyzing the measurements of the physiological parameters from the
first user over a desired time period; continuously monitoring
whether any of the measurements of the physiological parameters of
the first user has changed; generating a set of chart associated
with the desired time period that is representative of the
measurements of the physiological parameters; causing the wearable
device to send the set of chart to a telecommunication device
associated with a second user; receiving, from the second user, an
indication of which of the physiological parameters is to be
altered by the first user to assist with managing stress.
16. A method for determining an arousal condition of a subject, the
method comprising: (i) receiving, using a sensor, a physiological
signal from the subject; (ii) calculating, using a processor, a
value of a statistical variation of at least one physiological
parameter derived from the physiological signal, the at least one
physiological parameter includes pulse pressure; and (iii)
determining, using the processor, whether or not the calculated
value of the statistical variation of the at least one
physiological parameter lies outside of a threshold value and
therefore that the subject is in an arousal condition corresponding
to a stress condition, wherein the value of the calculated
statistical variation of the at least one physiological parameter
that lies outside of the threshold value is the value of the
statistical variation of the at least one physiological parameter
that has been determined to correspond to a more than 40% increase
in salivary cortisol of the subject.
17. The method of claim 16, wherein receiving the physiological
signal, further includes receiving, using the sensor, data related
to at least five consecutive cardiac cycles of the physiological
signal.
18. The method of claim 17, wherein the data includes time
intervals of the at least five cardiac cycle relating to a systolic
peak, start time and end time of each cardiac cycle.
19. The method of claim 16, wherein calculating, using the
processor, the value of the statistical variation of the at least
one physiological parameter, further includes calculating the
standard deviation of the pulse pressure.
20. The method of claim 19, wherein determining, using the
processor, whether or not the calculated value of the statistical
variation of the at least one physiological parameter lies outside
of the threshold value comprises determining if the calculated
standard deviation of the pulse pressure is greater than the
threshold value, in which the threshold value is within a range of
3.0-12.0 mmHg mercury level.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation of U.S. patent
application Ser. No. 15/537,967, filed Jun. 20, 2017 which is a
national phase entry under 35 U.S.C. .sctn. 371 of International
Application No. PCT/SG2015/050500, filed Dec. 23, 2015, published
in English, which claims priority to International Application No.
PCT/SG2014/000622, filed Dec. 30, 2014, published in English.
FIELD
[0002] The present invention relates to a method and apparatus for
deriving a mental state of a subject.
BACKGROUND
[0003] Emotional arousal is a human body's natural response to the
ever changing conditions around us. In the presence of stress
arousal, the human body instinctively produces "flight or fight"
stress (reaction) hormones such as adrenaline or cortisol.
Secretion of the stress hormones into the blood stream results in
characteristic physiological changes to the heart rate, blood
pressure, breathing as well as muscular functioning. These changes
help an individual stay focused, energetic and alert so that he/she
may react and cope with the situations correctly and
efficiently.
[0004] Although stress is considered essential and beneficial for
daily performance, chronic exposure to high levels of stress
hormones is also known to increase risk of hypertension,
immune-suppression, indigestion, depression, anxiety disorders,
loss of libido, headaches, loss of memory, lack of concentration,
learning difficulties, insomnia or the like.
[0005] FIG. 1 is a graph 100 showing one example of an empirical
relationship between arousal level and performance, which is known
as the Yerkes-Dodson curve, whereby the Yerkes-Dodson law dictates
that performance tends to increase with increasing levels of
arousal until a certain point and from thereon, decreases instead.
Research has also found that different tasks require different
levels of arousal for optimal performance and thus a shape of the
Yerkes-Dodson curve may be highly variable. For intellectually
demanding tasks, a lower level of arousal is required to enable
concentration, whereas for tasks that demand stamina or
persistence, a higher level of arousal is instead required to
facilitate motivation. A further example is shown in a graph 200 in
FIG. 2. For simple or well-learned tasks, the relationship between
arousal level and performance may be considered linear with
improvements in performance as the arousal level increases. But for
complex, unfamiliar or difficult tasks, the relationship between
arousal level and performance is reversed, whereby the performance
decreases as arousal level increases.
[0006] Depending how an individual reacts towards and perceives a
situation he/she encounters, stress is typically experienced in two
different forms. A positive form of stress, also known as eustress,
motivates and fuels the individual to achieve his/her peak
performance. Individuals experiencing this positive stress often
describe themselves as being "pumped up" or "being psyched". It is
also believed that there is an ideal amount of eustress that an
individual needs to experience in order to work at his/her optimum
level. Too little eustress and an individual may get bored, while
too much eustress may however lead the individual to act
recklessly, make poor decisions, and be worn down (both physically
and mentally). On the other hand, negative stress or distress, is
often associated with an individual experiencing negative emotions
such as anxiety or worry. This kind of negative stress undesirably
reduces performance and causes mental and physical drawbacks as
described above.
[0007] It is to be appreciated that a healthy heart typically has a
natural beat-to-beat variation in rate, known as Heart Rate
Variability (HRV). Patterns and rhythms within this variability are
important to health and general well-being. Research has shown that
when an individual shifts into a different emotional state, his/her
heart rhythms changes immediately. Specifically, negative emotions
(or negative valence), such as anxiety and frustration, are
associated with a disordered and chaotic variation. On the other
hand, positive emotions (or positive valence), such as calmness and
tranquillity, tend to show an ordered rhythm synchronized with
breathing which is also known as HRV coherence. Hence, an
individual who is able to control his/her HRV coherence may
subsequently see improved cognitive performance and emotional
well-being.
[0008] Indeed, it is thus important for an individual to be aware
of his/her emotional state so that necessary actions may be taken
to maintain his/her overall physical and mental well-being.
[0009] Presently, common methods employed by psychologists for
assessing emotional well-being typically involve interviews,
questionnaires as well as participant self-scoring. Results from
such tests often however suffer from judgemental errors from both
the observers and participants. Other methods of assessment may
include salivary cortisol testing, which can be rather troublesome
and time consuming, even though it is fairly accurate in detecting
stress. It is thus not feasible for an individual to continuously
monitor his/her emotional levels using conventional solutions,
especially for over an extended period of time.
[0010] One object of the present invention is therefore to address
at least one of the problems of the prior art and/or to provide a
choice that is useful in the art.
SUMMARY
[0011] According to a 1st aspect of the invention, there is
provided a method for deriving a mental state of a subject, the
method comprising: (i) receiving a bio-signal from the subject;
(ii) calculating respective statistical variations of at least two
physiological parameters derived from the bio-signal; (iii)
determining an arousal level of the subject based on the calculated
statistical variations of the at least two physiological
parameters; (iv) deriving a time-domain heart rate variability
signal from the bio-signal for calculating at least two heart rate
variability parameters; (v) determining a valence level of the
subject based on the at least two heart rate variability
parameters; and (vi) deriving the mental state from the arousal
level and valence level.
[0012] An advantage of the proposed method is that using the two
heart rate variability parameters, such as a LF/HF ratio and/or a
kurtosis value, and/or HRV coherence, enables a mental state of the
subject to be derived more accurately, consequently allowing
emotional states of the subject to be tracked over a required time
period.
[0013] Preferably, the at least two physiological parameters may be
selected from the group consisting of respiration rate, heart rate
and pulse pressure.
[0014] Preferably, receiving the bio-signal may include receiving
data related to at least five cardiac cycles of the bio-signal.
[0015] Preferably, the method may further comprise using a first of
the at least two heart rate variability parameters for determining
an initial valence level of the subject, and using a second of the
at least two heart rate variability parameters as a conditional
threshold parameter for verifying the initial valence level to
obtain the valence level in step (v).
[0016] Preferably, the at least two heart rate variability
parameters may be selected from the group consisting of a heart
rate variability coherence of the subject, a LF/HF ratio and a
kurtosis value.
[0017] Preferably, the heart rate variability coherence may be
calculated by correlating the time-domain heart rate variability
signal with a reference signal.
[0018] Preferably, the LF/HF ratio and kurtosis value may be
calculated from a frequency-domain signal obtained from the
time-domain heart rate variability signal.
[0019] Preferably, the frequency-domain signal may be obtained by
converting the time-domain heart rate variability signal into a
Power Spectral Density spectrum.
[0020] Preferably, the reference signal may include a sine wave
representing a time-domain reference signal.
[0021] Preferably, the data may include time intervals of the at
least five cardiac cycle relating to a systolic peak, start time
and end time of each cardiac cycle.
[0022] Preferably, the method may further include configuring a
machine learning classifier to collectively use the calculated
statistical variations for determining the arousal level.
[0023] Preferably, the machine learning classifier may include
Support Vector Machine, Naive Bayes or k-Nearest Neighbours.
[0024] According to a 2nd aspect of the invention, there is
provided a computer program for deriving a mental state of a
subject, the computer program downloadable to an electronic device
and includes a set of instructions, when executed, is arranged to
control a processor of the electronic device to: (i) receive a
bio-signal from the subject; (ii) calculate respective statistical
variations of at least two physiological parameters derived from
the bio-signal; (iii) determine an arousal level of the subject
based on the calculated statistical variations of the at least two
physiological parameters; (iv) derive a time-domain heart rate
variability signal from the bio-signal for calculating at least two
heart rate variability parameters; (v) determine a valence level of
the subject based on the at least two heart rate variability
parameters; and (vi) derive the mental state from the arousal level
and valence level.
[0025] Preferably, the computer program may be downloadable over
the internet.
[0026] According to a 3rd aspect of the invention, there is
provided a computer program stored in a memory of an electronic
device, the computer program having a set of instructions, when
executed, is arranged to control a processor of the electronic
device to: (i) receive a bio-signal from a subject; (ii) calculate
respective statistical variations of at least two physiological
parameters derived from the bio-signal; (iii) determine an arousal
level of the subject based on the calculated statistical variations
of the at least two physiological parameters; (iv) derive a
time-domain heart rate variability signal from the bio-signal for
calculating at least two heart rate variability parameters; (v)
determine a valence level of the subject based on the at least two
heart rate variability parameters; and (vi) derive a mental state
of the subject from the arousal level and valence level.
[0027] According to a 4th aspect of the invention, there is
provided an apparatus for deriving a mental state of a subject, the
apparatus comprising: (i) a receiver for receiving a bio-signal
from the subject; and (ii) a processor for: (a) calculating
respective statistical variations of at least two physiological
parameters derived from the bio-signal; (b) determining an arousal
level of the subject based on the calculated statistical variations
of the at least two physiological parameters; (c) deriving a
time-domain heart rate variability signal from the bio-signal for
calculating at least two heart rate variability parameters; (d)
determining a valence level of the subject based on the at least
two heart rate variability parameters; and (e) deriving the mental
state from the arousal level and valence level.
[0028] Preferably, the apparatus may be in the form of an
electronic device.
[0029] Preferably, the electronic device may be a
telecommunications device or a wearable device.
[0030] Preferably, the apparatus may include a wearable device and
a telecommunications device having a receiver; and wherein the
wearable device includes a signal sensing device for obtaining the
bio-signal from the subject, and a data processing module for
determining data relating to the bio-signal, wherein the receiver
of the telecommunications device is arranged to receive the
determined data of the bio-signal.
[0031] Preferably, the electronic device may include at least one
LED and one Photodiode, the LED arranged to emit light onto a
surface of the subject and the Photodiode arranged to receive light
reflected by the surface of the subject from the emitted light, the
reflected light corresponding to the data relating to the at least
five cardiac cycles of the bio-signal.
[0032] According to a 5th aspect of the invention, there is
provided a method for determining a valence level of a subject, the
method comprising: (i) receiving a bio-signal from the subject;
(ii) deriving a time-domain heart rate variability signal from the
bio-signal for calculating at least two heart rate variability
parameters; and (iii) determining the valence level from the at
least two heart rate variability parameters.
[0033] Preferably, the at least two heart rate variability
parameters may be selected from the group consisting of a heart
rate variability coherence of the subject, a LF/HF ratio and a
kurtosis value.
[0034] According to a 6th aspect of the invention, there is
provided a method for determining an arousal level of a subject,
the method comprising: (i) receiving a bio-signal from the subject;
(ii) calculating statistical variation of at least one
physiological parameter derived from the bio-signal; and (iii)
determining an arousal level of the subject based on the calculated
statistical variation of the at least one physiological parameter,
wherein the arousal level is determined to be in a stress level if
the calculated statistical variation correspond to more than 40%
increase in salivary cortisol of the subject.
[0035] Preferably, receiving the bio-signal may include receiving
data related to at least five consecutive cardiac cycles of the
bio-signal.
[0036] Preferably, the data may include time intervals of the at
least five cardiac cycle relating to a systolic peak, start time
and end time of each cardiac cycle.
[0037] Preferably, the at least one physiological parameter may
include pulse pressure.
[0038] Preferably, calculating the statistical variation of the at
least one physiological parameter may include calculating the
standard deviation of the pulse pressure.
[0039] Preferably, the arousal level may be determined to be in the
stress level if the calculated standard deviation of the pulse
pressure is greater than a threshold value, in which the threshold
value is within a range of about 3.0-12.0 mmHg mercury level.
[0040] Preferably, the arousal level may be determined to be in the
stress level if the calculated standard deviation of the pulse
pressure is greater than the threshold value, in which the
threshold value is within a range of about 6.0-10.0 mmHg mercury
level.
[0041] According to a 7th aspect of the invention, there is
provided a method for deriving a mental state of a subject, the
method comprising: (i) receiving a bio-signal from the subject;
(ii) calculating respective statistical variations of at least two
physiological parameters derived from the bio-signal; and (iii)
determining an arousal level of the subject based on the calculated
statistical variations of the at least two physiological
parameters.
[0042] Preferably, the at least two physiological parameters may be
selected from the group consisting of respiration rate, heart rate
and pulse pressure.
[0043] According to an 8th aspect of the invention, there is
provided a method for determining an arousal level of a subject,
the method comprising: (i) receiving a bio-signal from the subject;
(ii) calculating statistical variation of at least one
physiological parameter derived from the bio-signal; and (iii)
determining an arousal level of the subject based on the calculated
statistical variation of the at least one physiological parameter,
wherein the arousal level is determined to be in a stress level if
the calculated statistical variation is greater than a threshold
value, in which the threshold value is within a range of about
3.0-12.0 mmHg mercury level.
[0044] It should be apparent that features relating to one aspect
of the invention may also be applicable to the other aspects of the
invention.
[0045] These and other aspects of the invention will be apparent
from and elucidated with reference to the embodiments described
hereinafter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0046] Embodiments of the invention are disclosed hereinafter with
reference to the accompanying drawings, in which:
[0047] FIG. 1 is a first graph showing an empirical relationship
between arousal level and performance, according to the prior
art;
[0048] FIG. 2 is a second graph showing an empirical relationship
between arousal level and performance of easy and difficult tasks,
according to the prior art;
[0049] FIG. 3 is a schematic diagram of an apparatus for deriving a
mental state of a subject, according to an embodiment of the
invention;
[0050] FIG. 4 is a schematic block diagram of the apparatus of FIG.
3;
[0051] FIG. 5 is a schematic diagram of a LED-PD configuration for
a wearable device of the apparatus of FIG. 3;
[0052] FIG. 6 is a flow diagram of a method, performed by the
apparatus of FIG. 1, for determining an arousal level of a
subject;
[0053] FIG. 7 is a diagram of a PPG signal;
[0054] FIG. 8 is a table of various parameters showing associated
results related to accurately comparing stressful and non-stressful
situations;
[0055] FIG. 9, which includes FIGS. 9a to 9c, shows an improved
result for classifying arousal levels by using SVM;
[0056] FIG. 10 is a flow diagram of a method, performed by the
apparatus of FIG. 1, for determining a valence level of a
subject;
[0057] FIG. 11 is a graph showing a PSD spectrum;
[0058] FIG. 12 is a Venn diagram illustrating overlapping
relationships between different emotions classification;
[0059] FIG. 13, which includes FIGS. 13a and 13b, shows respective
diagrams illustrating differences in the HRV coherence and kurtosis
value during normal and calm states;
[0060] FIG. 14 is a Venn diagram illustrating a method of using
different HRV features for determining a valence level;
[0061] FIG. 15 is a flow diagram of a method, performed by the
apparatus of FIG. 1, for deriving a mental state of a subject;
[0062] FIG. 16 is a chart diagram used for classifying different
levels of arousal and valence into different emotional states;
[0063] FIG. 17 is a chart diagram used for more detailed
classifying different levels of stress and valence into different
emotional states; and
[0064] FIG. 18 is a table of measured mercury levels associated
with the SDPP parameter as empirically obtained under stressful and
non-stressful situations.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0065] FIG. 3 is a schematic diagram of an apparatus 300
(comprising a wearable device 302 and a telecommunication device
304) for deriving a mental state of a subject (not shown),
according to a first embodiment of the invention. FIG. 4 shows the
schematics of the apparatus 300. The subject is a user of the
apparatus 300. The wearable device 302 is realisable in different
forms, for example as a wrist watch. Of course, it will be
appreciated that in other envisaged embodiments, the wearable
device 302 may also be in any other form suitable to be worn on any
part of a user's body, such as his/her arms, ears, chest, fingers,
neck, or foot. In any event, it is to be appreciated that the
wearable device 302 is preferably arranged to be conveniently
portable, for example in a palm-sized form factor. The wearable
device 302 is configured to acquire physiological measurements from
a user (wearing the wearable device 302), derive a mental state
(which is also known as emotional state) of the said user and then
(wired/wirelessly) communicate the derived result(s) to the
telecommunication device 304. Examples of the telecommunication
device 304 include mobile phones, suitable portable electronic
devices, computing devices such as desktop computers, laptop
computers, tablets or the like.
[0066] In respect of FIG. 4, the wearable device 302 includes a
signal sensing module 402 for obtaining a bio-signal from the said
subject. In this case, the signal sensing module 402 is implemented
in the form of an LED-PD module 500 as shown in FIG. 5, but not to
be construed as limiting. The LED-PD module 500 includes an LED 502
and two (first and second) PDs 504a, 504b. It is to be appreciated
that the term "PD" represents photo diode. Specifically, for the
wearable device 302 implemented in the form of a wrist watch, the
LED-PD module 500 is configured for taking measurements in
reflectance mode. The measurements are based on an amount of light
emitted/transmitted by the LED 502 onto a surface (e.g. the wrist,
in the case of the wearable device 302 being a wrist watch) of the
subject, in which some of the emitted light will subsequently be
reflected by the surface as reflected light. This reflected light
is then detected by the two PDs 504a, 504b as the bio-signal of the
subject. That is, the signal sensing module 402 is thus an optical
measurement sensor.
[0067] Further, it is to be appreciated that the apparatus 300 also
includes other types of physiological parameters sensors (not shown
in FIG. 4) such as ECG, EEG, EMG, GSR, temperature sensor and/or
pressure sensor that are usable in conjunction with the signal
sensing module 402. The different physiological parameters sensors
may be arranged separate from the wearable device 302 or integrated
therewithin. The different physiological parameters sensors are
configured to obtain various physiological parameters (e.g. body
temperature and GSR) of the user, and may be positioned on the same
or different locations of the user's body (as the wearable device
302) during sensing.
[0068] The wearable device 302 further includes a data processing
and computational module 404 (hereinafter data processing module
for brevity), such as a processor, which is arranged to receive and
process the bio-signal acquired by the signal sensing module 402
into an output result. The wearable device 302 also includes a
display unit 406 for displaying the output result to the subject.
Moreover, the wearable device 302 includes a wired/wireless
transmission module 408 arranged to wired/wirelessly communicate
the output result to the telecommunications device 304. Needlessly
to say, the telecommunication device 304 includes a corresponding
wired/wireless receiver module 410 for receiving signals relating
to the output results from the wearable device 302 and a display
unit 412 for displaying the received results to the subject. For
good order, it will be appreciated that a user of the wearable
device 302 and telecommunication device 304 may be one same
individual or different individuals, but for ease of explanation in
this embodiment, it is assumed that the user operating the wearable
device 302 and telecommunication device 304 is the same
individual.
[0069] Referring first to FIG. 15, which is a flow diagram 1500 of
a method, performed by the apparatus 300 of FIG. 1, for deriving a
mental state of a subject, an overview of steps of the said method
is briefly outlined with detailed description of each step to
follow further below. At step 1502, the subject operates the
wearable device 302 to acquire a bio-signal signal of himself,
while at next step 1504, an emotional arousal level of the subject
is calculated by the wearable device 302, based on the acquired
bio-signal. Then at step 1506, an emotional valence level of the
subject is calculated by the wearable device 302, also based on the
acquired bio-signal. It is to be appreciated that sequence of steps
1504 and 1506 is interchangeable, with no impact on the final
results obtained at step 1508. Lastly, at step 1508, the wearable
device 302 determines and derives a mental state of the subject
based on the calculated arousal level and valence level. The
derived mental state is then transmitted in the form of a suitable
signal by the wearable device 302 to the telecommunication device
304 to be displayed to the subject himself.
[0070] With reference to step 1508, it is to be appreciated that
mental states (i.e. emotions) are classifiable into a
multi-dimensional space by combining obtained results of different
levels of arousal and valence, and this multi-dimensional space is
depicted in a chart diagram 1600 shown in FIG. 16. Particularly,
"Zone 1" 1602 and "Zone 2" 1604 of the multi-dimensional space in
FIG. 16 show that if a subject is classified under either zone,
then the subject is considered to be in a stressed state.
Specifically, "Zone 1" 1602 represents a "Distress" state
classification while "Zone 2" 1604 represents a "Eustress" state
classification. On the other hand, if the subject is classified
under "Zone 3" 1606 or "Zone 4" 1608, then he is considered to be
in a non-stressed state. "Zone 3" 1606 represents a "Calm" state
classification while "Zone 4" 1608 represents a "Normal" state
classification. It is to be appreciated that classification between
"Zone 1" 1602 and "Zone 2" 1604 or between "Zone 3" 1606 and "Zone
4" 1608 is determined by a LF/HF (Low Frequency/High Frequency)
ratio, to be further elaborated below. Moreover, to more accurately
define overlapping emotions, HRV coherence and kurtosis values are
also utilized to further assist with the classification. FIG. 17
shows another chart diagram 1700, in which afore described methods
are used to enable a more detailed classification of different
levels of arousal and valence into even more levels of emotional
states, such as "Angry", "Alert", "Excited", "Stress", "Normal",
"Happy", "Depressed", "Bored" or "Relaxed".
[0071] The above mentioned steps 1502-1506 of the method in FIG. 15
are now respectively described in greater detail set out below.
[0072] Determining a Level of Emotional Arousal
[0073] Referring now to FIG. 6, there is shown a flow diagram 600
of a method, performed by the apparatus 300 of FIG. 1, for
determining an emotional arousal level of a subject. So it will be
appreciated that the method of FIG. 6 in its entirety corresponds
to step 1504 of the method in FIG. 15. The method of FIG. 6 is
performed according to the following steps. At step 602, a
bio-signal is acquired from the subject, which is essentially step
1502 of the method in FIG. 15. Then at step 604, the acquired
bio-signal is filtered, and followed by dividing the acquired
bio-signal into a plurality of processing windows at step 606.
Further, at a next step 608, respective statistical variations
(e.g. standard deviation, mean shift, root-mean-square of
successive differences (RMSSD) or the like) of the plurality of
processing windows (obtained at step 606) relating to at least any
two of the following physiological parameters of the subject are
calculated: body temperature, skin temperature, galvanic skin
response (GSR), respiration rate, heart rate (HR) and pulse
pressure (PP). For example, the HR and PP may be selected, and
respective standard deviations of the HR and PP (i.e. SDHR and
SDPP) are calculated. Of course, if desired, more than two
physiological parameters may also be used to enhance the accuracy
of the final results obtained at step 610. It is also to be
appreciated that some of the physiological parameters are
determined from the acquired bio-signal, while certain other
physiological parameters are however obtained from the
physiological parameters sensors as afore described. Lastly, at
step 610, an emotional arousal level of the subject is computed and
determined based on the calculated statistical variations of the
selected at least two physiological parameters.
[0074] Detailed description of each step 602-610 of the method of
FIG. 6 is now set out below.
1. Step 602 of the Method
[0075] At step 602, signal acquisition is performed by the wearable
device 302 using the signal sensing module 402 to obtain the
bio-signal and in this embodiment, the bio-signal is an arterial
PPG waveform signal 700 (as depicted in an example in FIG. 7)
comprising at least five cardiac cycles. That is, the PPG signal
700 is to include at least five cardiac cycles. The at least five
cardiac cycles are also arranged consecutively. The PPG signal 700
includes time intervals of the at least five cardiac cycle relating
to a systolic peak, start time and end time of each cardiac cycle.
The PPG signal 700 may be acquired from any peripheral sites of a
subject's body such as the wrist and/or finger. It has been
empirically determined that an accuracy suitable for acquiring the
subject's bio-signal may for example, but without being limiting,
be achieved by configuring the signal sensing module 402 to employ
a 180 seconds measurement window to obtain the subject's
bio-signal.
2. Step 604 of the Method
[0076] The acquired PPG signal 700 is next passed to the data
processing module 404 for processing and calculation. The data
processing module 404 may include a predefined digital band pass
filter (not shown) for filtering noise and artifacts from the
acquired PPG signal 700 to produce a filtered PPG signal (not
shown).
3. Step 606 of the Method
[0077] At step 606, the filtered PPG signal is then segmented into
a plurality of processing windows to facilitate subsequent
processing. But of course, segmenting the filtered PPG signal into
the plurality of processing windows may not be necessary in other
embodiments, if not desired. In such instances, a statistical
variation of the whole measurement window (used in step 602 to
acquire the subject's bio-signal) is then computed instead. Going
back to this embodiment, each processing window is arranged to
include at least five (consecutive) cardiac cycles, and since it is
described in step 602 that the PPG signal 700 is to include at
least five cardiac cycles arranged consecutively, this thus means
that there is a minimum of one processing window to result from
step 606. The size of a processing window is defined as a specified
time duration of said window, but to be understood is an example
and therefore not to be construed as being the only definition
possible. For instance, relating to calculating the subject's SDPP
and SDHR, it has been determined that an accuracy suitable for
calculating the SDPP and SDHR may for example, but without
limitation, be achieved by utilizing a predetermined window size of
about 30 seconds depending on specific accuracy requirement. The
size of each processing window may also be adaptively configured
during the segmenting process so that more cardiac cycles (i.e.
more than five) are included in each processing window to ensure
high accuracy of results.
4. Step 608 of the Method
[0078] For step 608, as mentioned, the respective statistical
variations (e.g. standard deviation, mean shift, root-mean-square
of successive differences (RMSSD) or the like) of the plurality of
processing windows (obtained at step 606) relating to at least any
two of the following physiological parameters of the subject are
calculated: body temperature, skin temperature, galvanic skin
response (GSR), respiration rate, heart rate (HR) and pulse
pressure (PP). For example, the HR and PP may be selected, and
respective standard deviations of the HR and PP (i.e. SDHR and
SDPP) are calculated. Conventional methods are used for calculating
the statistical variations, and hence will not be elaborated herein
for brevity sake.
5. Step 610 of the Method
[0079] At step 610, the calculated statistical variations obtained
from step 608 (or just a single statistical variation, if the
entire measurement window is instead utilized for the calculation)
are provided to a machine learning classifier for classification
into different arousal levels. Any known machine learning
classifier such as Support Vector Machine (SVM), Naive Bayes,
k-Nearest Neighbors (KNN) or the like may be used independently/in
combination for classification of the arousal levels, provided the
classifiers to be adopted are pre-trained using same/similar
parameters to be used for the classification using clinical test
results. In this embodiment, SVM is selected as the classifier to
be used, due to the robustness and high accuracy performance of
SVM.
[0080] As mentioned, the pulse pressure may be selected as an
example of one of the physiological parameters to be used, and SDPP
may be used because of the significant difference determined in
related measurement values for stressful and non-stressful
situations. Hence, a more accurate differentiation between
stressful and non-stressful situations is possible using SDPP. FIG.
8 is a table 800 depicting a list of parameters and respective
associated classifying performance from a clinical study performed.
It will be appreciated from the said table 800 that a low p-value
obtained for SDPP clearly shows the most significant differences in
value between the stressful and non-stressful situations.
[0081] While SDHR and SDPP may be selected (as examples) for use in
the method of FIG. 6, FIG. 9a and FIG. 9b illustrate that there are
still however overlapping data in each parameter that cannot be
used to accurately classify between stressful and non-stressful
situations. To address this issue, the SVM classifier is utilized
to also classify the overlapping classes of data. The improved
results are shown in FIG. 9c in which the trained SVM classifier is
substantially able to distinguish between stressful and
non-stressful situations.
[0082] Determining a Level of Emotional Valence
[0083] Moving to FIG. 10, there is shown a flow diagram 1000 of a
method, performed by the apparatus 300 of FIG. 1, for determining a
valence level of the subject. It will be appreciated that the
method of FIG. 10 in its entirety corresponds to step 1506 of the
method in FIG. 15. The method of FIG. 10 is performed according to
the following steps. At step 1002, the bio-signal signal is
acquired, which is essentially step 1502 of the method in FIG. 15.
So, steps 602 and 1002 are in fact just step 1502 of FIG. 15. Then
at step 1004, the acquired bio-signal is filtered, and followed by
extracting a time-domain heart rate variability (HRV) signal from
the acquired bio-signal at step 1006. At step 1008, a HRV coherence
of the subject is calculated by correlating the HRV signal with a
reference signal. The reference signal is, for example, a sine wave
representing a time-domain reference signal. Next, at step 1010, a
frequency domain analysis is performed (using Fast Fourier
Transform (FFT) algorithm) on the time-domain HRV signal to derive
a PSD graph to calculate at least one related frequency-domain
parameter. The at least one frequency-domain parameter includes a
LF/HF (Low Frequency/High Frequency) ratio and a kurtosis value,
but in alternative embodiments, other suitable features may also be
included. Lastly, at step 1012, a valence level of the subject is
determined (in this instance) based on the calculated HRV
coherence, LF/HF ratio, and kurtosis value. It is to be appreciated
that the HRV coherence, LF/HF ratio, and kurtosis value may also
collectively be termed as HRV parameters/features.
[0084] It is to be appreciated that step 1004 is, mutatis mutandis,
similar to step 604 of the method of FIG. 6 (as afore described),
and hence for sake of brevity, the explanation will not be
repeated. Detailed description of the subsequent steps 1006-1012 of
the method of FIG. 10 is now set out below.
1. Steps 1006 and 1008 of the Method
[0085] For steps 1006 and 1008, the teachings of a PCT application,
having publication number WO2014/031082, is incorporated in its
entirety herein. Hence, the interested reader is directed to the
afore said PCT application WO2014/031082 for full details on how to
derive a time-domain HRV signal from the bio-signal for
subsequently correlating with a reference signal in order to obtain
the HRV coherence of the subject. Of course, it is to be
appreciated that any other known methods for calculating the HRV
coherence (whether in time or frequency domain) may also be used,
and not just limited to only the method described in PCT
application WO2014/031082.
2. Step 1010 of the Method
[0086] At step 1010, by utilizing the Fast Fourier Transform (FFT)
algorithm, the time-domain HRV signal is converted into a Power
Spectral Density (PSD) graph by the data processing module 404, in
which an example graph 1100 of a PSD is shown in FIG. 11. That is,
the PSD graph is a frequency-domain spectrum obtained from the
time-domain HRV signal. Specifically, the low frequency (LF) power
(i.e. 0.04-0.15 Hz) and the high frequency (HF) power (i.e.
0.15-0.4 Hz) bands of the PSD graph are quantified by mathematical
integration of the PSD graph, which are then utilized by the data
processing module 404 to calculate a ratio of the low frequency
power to the high frequency power (LF/HF). Further, a spectral
kurtosis analysis is performed by the data processing module 404 on
the PSD graph to calculate a kurtosis value.
3. Step 1012 of the Method
[0087] It is to be appreciated that the LF/HF ratio is commonly
used to differentiate emotions of different levels of valence. But
as depicted in shaded portions of a Venn diagram 1200 in FIG. 12,
there is still overlapping of closely related emotions based on a
same value of LF/HF ratio. Apart from the LF/HF ratio, the HRV
coherence is also determined to be closely related to the
(emotional) valence level. For the kurtosis analysis of the PSD, as
shown in FIGS. 13a and 13b, the kurtosis value is found to be
highly related to the HRV coherence of an individual. For this
embodiment, it is thus determined that a combined analysis of the
HRV coherence, LF/HF ratio and kurtosis value provides the highest
accuracy in determining a valence level of the subject.
[0088] So, at step 1012, the valence level is derived collectively
from the HRV coherence, LF/HF ratio and kurtosis value. As
mentioned, a value of the LF/HF ratio typically indicates a
corresponding emotional valence of a subject but it is to be
appreciated that a boundary that separates closely related
emotional states (i.e. see FIG. 16 or FIG. 17) is still vague.
Nonetheless, an initial level of the valence is still determinable.
Subsequently, the HRV coherence and kurtosis value are used to
further assist with verifying that the initially determined valence
level is correct. For example, if the subject is in a calm state
(i.e. high autonomic balance), the valence level is characterized
by a high LF/HF ratio. To illustrate, with reference to a Venn
diagram 1400 in FIG. 14, a subject who is in a normal state but is
determined to have a higher value of the LF/HF ratio may otherwise
be mistaken to be in a calm state. Indeed, by incorporating the HRV
coherence and kurtosis values (whether individually or combined)
into the analysis as threshold parameters (which are compared
against threshold values), it may then be further verified whether
the subject is truly in a calm state. For good order, it is to be
appreciated that the threshold values are predetermined empirically
from clinical tests. For example, the following set of inequality
equations may be used to determine whether a calculated valence
level falls in the normal region: IF "calculated LF/HF>X",
"calculated Kurtosis <A" and "calculated HRV Coherence <B",
THEN "Valence Level=Normal", in which variables "A" and "B"
represent the predetermined threshold values, while "X" represents
just a predetermined value.
[0089] The remaining configurations will be described hereinafter.
For the sake of brevity, description of like elements,
functionalities and operations that are common between the
different configurations are not repeated; reference will instead
be made to similar parts of the relevant configuration(s).
[0090] In a second embodiment, instead of being two separate
devices (i.e. the wearable device 302 and telecommunication device
304), the apparatus 300 itself may be implemented as a single
equivalent electronic device, in which the wearable device 302 and
telecommunication device 304 are (hardware) integrated and
configured to perform all the same functions described in the first
embodiment. Further, all the steps 1502-1508 in the flow diagram
1500 of FIG. 15 (which thus also include steps 602-612 in the flow
diagram 600 of FIG. 6, and steps 1002-1012 in the flow diagram 1000
of FIG. 10) may be implemented as a computer program product
downloadable over the internet for storing on a memory of the said
electronic device. In other words, if there are improvements to the
method of FIG. 15, the electronic device may also be updated (as
and when required) with those improvements by way of the downloaded
computer program product.
[0091] In a third embodiment, all the steps 1502-1508 in the flow
diagram 150 of FIG. 15 may be performed by one electronic device
which may be the telecommunications device 304 (instead of the
wearable device 302), or other envisaged suitable electronic
devices. In other words, it is envisaged that the various
modules--signal sensing, data processing module 402, 404 may form
parts of the same electronic device, possibly as part of the
telecommunications device 304. As a result, there is no longer any
necessity for the wired/wireless transmission module 408 or
wired/wireless receiver module 410 in such a configuration.
Needless to say, the single electronic device may also be realised
as a wearable sensing device to be worn on the subject's body.
[0092] In a fourth embodiment, steps 1504-1508 of the method in
FIG. 15 may alternatively be performed by a data processing module
(not shown) of the telecommunications device 304, instead of the
wearable device 302, if it is determined (for example) that the
data processing module of the telecommunications device 304
possesses a higher processing power than that of the wearable
device 302, but however not to be construed as a limiting.
Furthermore, performance of steps 1504-1508 of FIG. 15 may also
dynamically be allocated between the data processing module of the
telecommunications device 304, and the data processing module 404
of the wearable device 302, depending on a desired configuration
effected by a user of the apparatus 300.
[0093] In summary, via the method of FIG. 15 (which is performed by
the proposed apparatus 300 of FIG. 3), a valence level of a subject
is obtained from a HRV signal thereof. Both the time and frequency
domains of the HRV signal are utilized during the process.
Specifically, from the time-domain, a HRV coherence of the subject
is determined. Also, a PSD graph is obtained by converting the HRV
signal from the time-domain to the frequency-domain. Therefrom, a
LF/HF ratio and a kurtosis value are derived from the PSD graph.
Subsequently, by analysing the HRV coherence, LF/HF ratio and
kurtosis value in combination, a valence level of the subject is
determined. In addition, an emotional arousal level of the same
subject is also determined from the statistical variations of the
heart rate and pulse pressure of the subject. But as afore
discussed, any other two physiological parameters from the group:
body temperature, skin temperature, galvanic skin response (GSR),
respiration rate are selectable as well. A machine learning
classifier (e.g. SVM) that has been trained to classify different
arousal levels using the same afore parameters is used to determine
an arousal level of the subject based on (for example) the measured
statistical variations of heart rate and pulse pressure. Based on
the determined valence and arousal levels of the subject, a mental
state of the user is consequently derived.
[0094] In a fifth embodiment, at step 610 of the method of FIG. 6,
an alternative to providing the calculated statistical variations
(obtained from step 608) to the machine learning classifier for
classifying into different arousal levels, is instead utilised.
Specifically, FIG. 18 is a table 1800 of measured mercury level
associated with the SDPP parameter as empirically obtained under
stressful and non-stressful situations, in relation to classifying
performance based on a clinical study using salivary cortisol as an
indicator for stress conditions. According to the study result, the
standard deviation for pulse pressure (PP) of test subjects is
observed to be higher during stressful conditions (i.e. see legend
label: "2--Mental stress test" in FIG. 18) corresponding to more
than 40% increase (i.e. >40%) in salivary cortisol, as compared
to non-stressful conditions (i.e. see legend labels: "1--Resting",
and "3--Recovery" in FIG. 18). Particularly, it is to be
appreciated that for SDPP, the more than 40% increase (i.e.
>40%) in salivary cortisol empirically corresponds to a cut-off
threshold range of about 3.0-12.0 mmHg mercury level measured, and
more specifically, that range may further be within a range of
about 6.0-10.0 mmHg mercury level. Preferably, a cut-off threshold
value of about 8.0 mmHg mercury level may beneficially be used as
an accurate indicator, if necessary. So, using the said cut-off
threshold value, the machine learning classifier is thus not
required in this embodiment.
[0095] Then drawing reference to FIG. 16, it consequently means
that the determined arousal level of a subject moves from "Zone 4"
1608 to "Zone 1" 1602, if there is a measured >40% increase in
salivary cortisol of the subject. Accordingly, the good correlation
results show that PP may serve as a good substitute parameter for
salivary cortisol testing for stress. Hence, in this embodiment,
(occurrence of) stress is defined as corresponding to detection of
>40% increase in salivary cortisol. Following from the preceding
statement, it may broadly be generalised that the arousal level of
a subject is determined to be in a stress level, if the calculated
statistical variation of at least one physiological parameter (e.g.
PP) corresponds to more than 40% increase in salivary cortisol of
the subject. Also, it may be said to the same effect that the
arousal level is determined to be in a stress level if the
calculated statistical variation is greater than a threshold value,
in which the threshold value is within a range of about 3.0-12.0 mm
Hg mercury level. The threshold value may be about 8.0 mmHg mercury
level, for example. Specifically, the calculated standard deviation
of the pulse pressure is determined to correspond to a range of
about 3.0-12.0 mmHg mercury level in order to determine that the
arousal level is in the stress level (i.e. >40% increase in
salivary cortisol). More preferably, the calculated standard
deviation of the pulse pressure is determined to correspond to a
threshold value of about 8 0 mmHg mercury level, under such an
instance. Also, to reiterate, the method 600 of FIG. 6 may be
carried out independent of the method 1500 of FIG. 15. Further, the
apparatus 300 of FIG. 1 may also be configured to provide an
independent readout of the determined arousal level (i.e. via the
display unit 406 of the wearable device 302), if necessary.
[0096] Applications
[0097] The proposed apparatus 300 beneficially provides a tracking
solution for individuals to track their emotional states over a
desired time period (in which the apparatus 300 may be programmed
to enable automatic tracking for that time period). This enables
the individuals to recognize situations which can cause emotional
changes to them so that the individuals are able to learn to cope
with those situations better in the future. The time period is
configurable to be any duration, such as the past hour or last 24
hours, last 7 days or last 1 month, as intended by the individuals.
Data generated by the apparatus 300 from the tracking, during the
required time period, may be presented to the individuals in
textual or graphical format such as trends displayed as bar or pie
charts.
[0098] Also, the proposed apparatus 300 may provide individuals
with bio-feedback, trainings or interventions to assist with
managing stress or improving into one of the healthy mental states.
One example is using breathing training to improve the HRV
coherence as taught in the PCT application, having publication
number WO2014/031082. Briefly, the wearable device 302 may also
include an audio output module (e.g. in the form of a speaker), in
which instructions for the breathing training are given to users
via the display unit 406 and the audio output module. Of course,
the instructions for the breathing training may also be
administered through the telecommunication device 304 or any other
suitable external devices. Other examples include configuring
audio/visual stimulations (effected via the apparatus 300) to
assist an individual to relax, and/or using interventions
(similarly effected via the apparatus 300) such as generating
vibrations, alerts and/or emails to alert the individual of
prolonged stress exposures.
[0099] Further, the apparatus 300 is able to help with monitoring
and altering emotions, and, as will be appreciated, is thus
especially useful in situations where emotions plays an important
part to the outcome of a situation in concern. As different tasks
require varying levels of emotional arousal and valence, using the
apparatus 300 therefore allows individuals to more easily and
knowingly alter their emotions so that their performance of the
related tasks may greatly be enhanced. As an example, by using the
apparatus 300, it may help working adults/students detect and
decrease their arousal level at any time they think is hampering
their related performance, such as attention level or creative
thinking. Similarly, companies may use the proposed apparatus 300
to help monitor the emotional states of their staffs, such as
whether a particular staff is facing problem(s) like a burnout or
is lacking motivation (i.e. low arousal) to perform his/her job
well, and accordingly determine whether remedy measures are
required to help that staff.
[0100] As another example, say in a physician-patient relationship
setting, the patient may also use the apparatus 300 to monitor and
manage his/her emotions, while the physician may beneficially track
the patient's emotion states over a period of time and/or correlate
with other behavioural data, such as activity and sleep tracking
data, for making better diagnosis for effective treatment.
[0101] The proposed apparatus 300 is also beneficially applicable
to the sports sector. While it is important for athletes to be
psyched up (i.e. positively aroused) to achieve optimum performance
during competitions, it is entirely a different matter if the
athletes are attempting to learn/master a new skill/technique,
where a calm mind (i.e. low arousal, and positive valence) is more
beneficial. For coaches, on the other hand, being able to analyse
the emotional states and trends of their athletes may be useful for
understanding the effectiveness of trainings conducted, as well as
aiding the coaches with selecting suitable athletes for
participating in competitions.
[0102] As a comparison, while there are many conventional devices
that enable users to measure their own emotional levels, most of
these devices however based measurements only via a single common
parameter such as the HRV, unlike the proposed apparatus 300 of
FIG. 3. Hence, the knowledge gap that exists in conventional
solutions is advantageously addressed by the apparatus 300 of FIG.
3, which purposively utilises the complex relationship between
physiological parameters and emotional states for accurate
determination of the emotional well-being of an individual.
[0103] While the invention has been illustrated and described in
detail in the drawings and foregoing description, such illustration
and description are to be considered illustrative or exemplary, and
not restrictive; the invention is not limited to the disclosed
embodiments. Other variations to the disclosed embodiments can be
understood and effected by those skilled in the art in practising
the claimed invention. For example, the LED-PD module 500 of FIG. 5
may alternatively also include a plurality of similar LEDs 502
paired with one or multiple PDs. That is, the basic configuration
of the LED-PD module 500 includes at least one LED and one PD. In
addition, for the method in FIG. 10, the least one threshold value
may also include a combination of other suitable parameters, other
than the LF/HF ratio and kurtosis value. Yet moreover, in certain
embodiments, either only the LF/HF ratio or kurtosis value may be
used together with the HRV coherence to derive the valence level,
even though the results obtained may be less accurate. Furthermore,
only one of the LF/HF ratio or kurtosis value or HRV coherence may
be used to derive the valence level, if desired. Also, at step 608
of FIG. 6, an average statistical variation may alternatively be
obtained by averaging all computed statistical variations of the
plurality of processing windows. Then, this average statistical
variation is provided (at step 610) to the machine learning
classifier for classification into different arousal levels.
[0104] Moreover, to derive the valence level, at least any two HRV
parameters may be selected from the group comprising of the HRV
coherence, LF/HF ratio, kurtosis value and other suitable
parameters may be used in certain embodiments. In such cases, a
first of the selected two HRV parameters may be used for
determining an initial valence level of the subject, and a second
of the selected two HRV parameters may then be used as a
conditional threshold parameter for verifying the initial valence
level to obtain the final valence level (at step 1012 of FIG. 10).
For example, the HRV coherence is used (e.g. instead of LF/HF) to
define an initial valence, while the kurtosis value and/or the
LF/HF ratio are/is used as conditional thresholds to further assist
with the verification. In another example, the LF/HF ratio is used
to define an initial valence, while only the kurtosis value is used
as a conditional threshold for the verification.
* * * * *