U.S. patent application number 17/132503 was filed with the patent office on 2022-01-20 for predicting orthosomnia in an individual.
The applicant listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to Boomika KALYAN, Evan Joel KASEMAN, Sara KRON, Benjamin Irwin SHELLY.
Application Number | 20220020492 17/132503 |
Document ID | / |
Family ID | |
Filed Date | 2022-01-20 |
United States Patent
Application |
20220020492 |
Kind Code |
A1 |
KRON; Sara ; et al. |
January 20, 2022 |
PREDICTING ORTHOSOMNIA IN AN INDIVIDUAL
Abstract
A mechanism for identifying orthosomnia, being the preoccupation
with sensor-derived sleep measures or automated sleep analysis
processes, within an individual. Interactions between the
individual and a software application performing a sleep analysis
process are monitored and used to generate a predictive indicator
that indicates a likelihood that the individual has orthosomnia.
This predictive indicator may be used to control the software
application, and in particular, to control the display of
information provided by the software application.
Inventors: |
KRON; Sara; (Pittsburgh,
PA) ; KALYAN; Boomika; (Pittsburgh, PA) ;
SHELLY; Benjamin Irwin; (Pittsburgh, PA) ; KASEMAN;
Evan Joel; (Monroeville, PA) |
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Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
EINDHOVEN |
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NL |
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Appl. No.: |
17/132503 |
Filed: |
December 23, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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63054197 |
Jul 20, 2020 |
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International
Class: |
G16H 50/20 20060101
G16H050/20; G06N 20/00 20060101 G06N020/00; G16H 50/70 20060101
G16H050/70; G16H 50/30 20060101 G16H050/30 |
Claims
1. A computer-implemented method for generating a predictive
indicator that indicates a likelihood of orthosomnia in an
individual, the computer-implemented method comprising: obtaining
user interaction data, being data responsive to the individual's
interactions, via a user interface for a processor, with a software
application running on the processor for performing a sleep
analysis or assessment process; and processing the obtained user
interaction data to generate the predictive indicator that
indicates a likelihood of orthosomnia in the individual.
2. The computer-implemented method of claim 1, wherein the user
interaction data comprises access data responsive to the individual
accessing and/or opening the software application.
3. The computer-implemented method of claim 2, wherein the access
data comprises one or more measures of accessing and/or opening
frequency and/or duration of access.
4. The computer-implemented method of claim 2, wherein the step of
processing the obtained user interaction data comprises: obtaining
population access data, being data that is responsive to population
trends of other individual's interactions with the software
application running on the processor and/or one or more other
versions of the software application running on one or more other
processors; and comparing the access data to the population access
data to generate the predictive indicator.
5. The computer-implemented method of claim 2, wherein the access
data comprises time data indicating a time at which the individual
interacts with the software application, and wherein the step of
processing the obtained user interaction data comprises: obtaining,
from a biometric sensor, biometric data of the user, the biometric
data being responsive to changes in one or more physiological
parameters of the individual at or during a time at which the
individual accesses the and/or opens the software application, as
indicated by the time data; and processing the biometric data to
generate the predictive indicator.
6. The computer-implemented method of claim 5, wherein the one or
more physiological parameters of the individual comprise a
physiological parameter responsive to an autonomic response of the
individual, such as a heartrate, sweating, a temperature, a
respiratory rate, skin color, eye movement and/or an eye
dilation.
7. The computer-implemented method of claim 2, wherein the step of
processing the obtained user interaction data comprises: obtaining
historic access data, being data responsive to the individuals
historic interactions, via the user interface, with the software
application; and comparing the access data to the historic access
data to generate the predictive indicator.
8. The computer-implemented method of claim 1, wherein: the step of
obtaining the user interaction data comprises obtaining, via the
user interface, user-derived sleep quality data representing a
subjective perception of sleep quality of the individual; and the
step of processing the obtained user interaction data comprises:
obtaining, from a sleep sensor, sleep sensor data responsive to one
or more changes of physiological parameters of the individual
during the individual's sleep; processing the sleep sensor data to
generate sensor-derived sleep quality data, being data representing
an objective measure of the sleep quality of the individual; and
comparing the user-derived quality input data to the sensor-derived
sleep quality data to generate the predictive indicator.
9. A computer-implemented method of controlling a software
application running on a processor for performing a sleep analysis
or assessment process, the computer-implemented method comprising:
generating a predictive indicator that indicates a likelihood of
orthosomnia in an individual by performing the method of claim 1;
and controlling the software application to modify and/or
supplement information presented to the individual, via the user
interface, during the sleep analysis or assessment process based on
the predictive indicator.
10. The computer-implemented method of claim 9, wherein the
software application is configured to generate and display, at the
user interface, a sleep quality measure during the sleep analysis
or assessment process, and the step of controlling the software
application comprises at least one of: modifying the sleep quality
measure in response to the predictive indicator indicating that a
likeliness of orthosomnia in the individual falls within a first
predetermined range; suppressing the display of the sleep quality
measure in response to the predictive indicator indicating that a
likeliness of orthosomnia falls within a second predetermined
range; and/or providing, at the display, supplementary information
about sleep quality measures in response to the predictive
indicator indicating that a likeliness of orthosomnia falls within
a third predetermined range.
11. The computer-implemented method of claim 10, wherein: the step
of obtaining the user interaction data comprises obtaining, via the
user interface, user-derived sleep quality data representing a
subjective perception of sleep quality of the individual: the
computer-implemented method further comprises: obtaining, from a
sleep sensor, sleep sensor data responsive to one or more changes
of physiological parameters of the individual during the
individual's sleep; and processing the sleep sensor data to
generate sensor-derived sleep quality data, being data representing
an objective measure of the sleep quality of the individual,
wherein the supplementary information comprises information
responsive to a difference between the user-derived sleep quality
data and the sensor-derived sleep quality data.
12. The computer-implemented method of claim 1, wherein the
computer-implemented method is performed by the software
application.
13. The computer-implemented method of claim 1, wherein the
predictive indicator comprise a binary indicator indicating a
prediction of whether or not the individual has orthosomnia.
14. A computer program product comprising computer program code
means which, when executed on a computing device having a
processing system, cause the processing system to perform all of
the steps of the method according to claim 1.
15. A processing system for generating a predictive indicator that
indicates a likelihood of orthosomnia in an individual, the
processing system being configured to: obtain user interaction
data, being data responsive to the individual's interactions, via a
user interface for a processor, with a software application running
on the processor for performing a sleep analysis or assessment
process; and process the obtained user interaction data to generate
the predictive indicator that indicates a likelihood of orthosomnia
in the individual.
Description
CROSS-REFERENCE TO PRIOR APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application No. 63/054,197, filed on 20 Jul. 2020. This application
is hereby incorporated by reference herein.
FIELD OF THE INVENTION
[0002] The present invention relates to the field of sleep
analysis, and in particular, to the field of sleep analysis
performed by an individual without clinician input.
BACKGROUND OF THE INVENTION
[0003] The use of (wearable) sleep trackers is being more popular
and widespread, and are often used to perform sleep analysis or
assessment without the input of a clinician/physician.
[0004] Sleep trackers often execute a software application, running
on the processor, to perform a sleep analysis or assessment
process, e.g. based upon biometric data gathered by the sleep
tracker itself or an external sensor, such as movement data from an
accelerometer (e.g. mounted on the individual's wrist) or heartrate
data from a heartrate monitor.
[0005] However, despite their widespread use, the accuracy of sleep
trackers has been brought into question. Studies evaluating the
ability of wearable trackers to estimate sleep have generally found
high sensitivity, comparatively low specificity, and invalidity
around wake times during sleep. Additionally, even when users are
presented with accurate sleep tracking data within a healthy normal
range, users may lack the domain knowledge to appropriately
interpret the information provided to them.
[0006] Moreover, a disconnect between sleep quality information
provided by a sleep tracker and a subjective analysis of sleep
quality performed by the individual has been observed. This is at
least partly because an individual's subjective evaluation of their
sleep quality is largely dependent on sleep onset latency (SOL) and
the frequency and duration of wakefulness during the night,
estimates of which vary from objective measures obtained using a
sleep tracker.
[0007] This disconnect can cause anxiety and concern in an
individual who uses a sleep tracker, and there is an ongoing desire
to address this problem.
SUMMARY OF THE INVENTION
[0008] The invention is defined by the claims.
[0009] According to examples in accordance with an aspect of the
invention, there is provided a computer-implemented method for
generating a predictive indicator that indicates a likelihood of
orthosomnia in an individual.
[0010] The computer-implemented method comprises: obtaining user
interaction data, being data responsive to the individual's
interactions, via a user interface for a processor, with a software
application running on the processor for performing a sleep
analysis or assessment process; and processing the obtained user
interaction data to generate the predictive indicator that
indicates a likelihood of orthosomnia in the individual.
[0011] Orthosomnia is a condition characterized by a preoccupation
or obsession with improving or perfecting sleep quality data
generated by a (computer-executed) sleep analysis or assessment
process, and in particular to sensor-derived sleep quality data
(being a prediction of sleep quality of the individual generated
based on biometric/physiological data of the individual).
[0012] The present disclosure recognizes that a user interaction
with a software application running or performing a sleep
analysis/assessment process can be indicative of an individual's
preoccupation with data generated by the software application. In
particular, a manner in which a user interacts with the software
application, or the information that is supplied to the software
application by the user, is indicative of possible orthosomnia in
the individual.
[0013] The present disclosure proposes an approach for identifying
possible orthosomnia in the individual, by assessing/analyzing user
interaction data responsive to the user's interaction with the
software application. The user's interaction is via a user
interface, which may be formed in the same device that comprises
the processor that carries out or runs the software application
(e.g. a mobile/cellular phone) or a separate device (e.g. the
processor is a distributed processing system and the user interface
is a mobile/cellular phone in communication with the distributed
processing system).
[0014] In one example, the computer-implemented method comprises
displaying the predictive indictor via the user interface. This
provides an individual/clinician with additional information to aid
them in performing a treatment/diagnostic process.
[0015] In some examples, the user interaction data comprises access
data responsive to the individual accessing and/or opening the
software application. This embodiment recognizes that the
characteristics of the user accessing or opening the software
application are indicative of whether or not individual has
orthosomnia.
[0016] For instance, the access data may comprise one or more
measures of accessing and/or opening frequency and/or duration of
access. This embodiment recognizes a link between the amount of
time that a user accesses the software application (and/or a
frequency of access) and likelihood of orthosomnia. In particular,
it is recognized that an individual who frequently accesses the
software application, or accesses the software application for a
long period of time, is more likely to have orthosomnia than an
individual who less frequently, or for shorter periods of time,
accesses the software application.
[0017] Frequent and/or long accesses thereby indicate a high
likelihood of orthosomnia. Low and/or short accesses similarly
indicate a low likelihood of orthosomnia.
[0018] The one or more measures may relate to a time or frequency
of accessing particular parts, screens or displays of the software
application, e.g. a part that displays sleep quality data. It is
recognized that a preoccupation with certain elements of the
software application (such as the quality data) may be indicative
of potential orthosomnia.
[0019] In some examples, the step of processing the obtained user
interaction data comprises: obtaining population access data, being
data that is responsive to population trends of other individual's
interactions with the software application running on the processor
and/or one or more other versions of the software application
running on one or more other processors; and comparing the access
data to the population access data to generate the predictive
indicator.
[0020] The population access data may thereby provide a benchmark,
baseline or average expected level of accesses to the software
application. In particular, a population trend of other
individual's interactions with the software application (or other
instances/versions of the software application) provides
information establishing a baseline against which a likelihood of
orthosomnia can be predicted. For instance, a strong (positive)
deviation from a mean of a population could indicate the individual
is accessing the software application more commonly than expected,
indicating possible orthosomnia.
[0021] This approach thereby provides an accurate mechanism for
assessing a likelihood of orthosomnia in the individual.
[0022] The access data may comprise time data indicating a time (of
day) at which the individual interacts with the software
application. The time data may, for example, comprise one or more
time stamps or other similar data structures.
[0023] In some examples, this time data may be processed by itself
to generate the predictive indicator. This embodiment recognizes
that a time at which an individual interacts with the software
application (as a whole, or with certain parts of the software
application) may be indicative of possible orthosomnia. For
instance, an individual is more likely to have orthosomnia if they
interact with the software application during the night (e.g. as
this would demonstrate an anxiety about their quality of sleep) or
repeatedly throughout the day (e.g. as this would indicate a
preoccupation with their purported quality of sleep).
[0024] In some examples, the step of processing the obtained user
interaction data comprises: obtaining, from a biometric sensor,
biometric data of the user, the biometric data being responsive to
changes in one or more physiological parameters of the individual
at or during a time at which the individual accesses and/or opens
the software application, as indicated by the time data; and
processing the biometric data to generate the predictive
indicator.
[0025] This embodiment recognizes that a physiological response of
the individual during an accessing of the software application is
indicative of potential orthosomnia. For instance, if the
individual reacts nervously or negatively during access of the
software application, this could indicate potential
orthosomnia.
[0026] The one or more physiological parameters of the individual
comprise a physiological parameter responsive to an autonomic
response of the individual, such as a heartrate, sweating, a
temperature, a respiratory rate, skin color, eye movement and/or an
eye dilation. These embodiments recognize that an involuntary
response (autonomic response) may indicate a nervousness or concern
with the information provided by the software application, and
thereby indicates a likelihood of orthosomnia. The proposed
approach thereby provides a user independent mechanism, i.e. an
objective mechanism, for predicting a likelihood of
orthosomnia.
[0027] In at least one embodiment, the step of processing the
obtained user interaction data comprises: obtaining historic access
data, being data responsive to the individual's historic
interactions, via the user interface, with the software
application; and comparing the access data to the historic access
data to generate the predictive indicator.
[0028] A change in how a user accesses or opens the software
application over time may indicate a change in potential
orthosomnia. For instance, if a user is accessing the software
application more frequently than in the past, then this may
indicate that the probability of the user having orthosomnia is
increasing.
[0029] In some examples, the step of obtaining the user interaction
data comprises obtaining, via the user interface, user-derived
sleep quality data representing a subjective perception of sleep
quality of the individual.
[0030] The step of processing the obtained user interaction data
may comprise: obtaining, from a sleep sensor, sleep sensor data
responsive to one or more changes of physiological parameters of
the individual during the individual's sleep; processing the sleep
sensor data to generate sensor-derived sleep quality data, being
data representing an objective measure of the sleep quality of the
individual; and comparing the user-derived sleep quality data to
the sensor-derived sleep quality data to generate the predictive
indicator.
[0031] This embodiment recognizes that a difference between
sensor-derived sleep quality data and user-derived sleep quality
data indicates potential orthosomnia or an increased risk of
orthosomnia. In particular, if there is a large difference between
an individual's perception of their sleep, and the quality of sleep
derived from sensor data, then there is an increased likelihood
that the individual will become anxious about the quality data
produced by the software application--which may distort their own
perception of their sleep quality, leading to orthosomnia.
[0032] There is also proposed a computer-implemented method of
controlling a software application running on a processor for
performing a sleep analysis or assessment process.
[0033] The computer-implemented method comprises: generating a
predictive indicator that indicates a likelihood of orthosomnia in
an individual by performing any suitable herein described method;
and controlling the software application to modify and/or
supplement information presented to the individual, via the user
interface, during the sleep analysis or assessment process based on
the predictive indicator.
[0034] This approach recognizes that the effects of orthosomnia can
be mitigated or reduced by controlling the information presented to
the individual (at the user interface) by the software application.
Through appropriate control of the software application, the
individual can be provided with suitable information to assuage
their concerns or anxieties and/or reduce the likelihood of
orthosomnia in the individual.
[0035] The skilled person would appreciate that, in another aspect
of the inventive concept, the method of generating the predictive
indicator (for the method of controlling the software application)
may be carried out by a process not disclosed in this
specification. For instance, the predictive indicator may be an
indicator received from a clinician (indicating a diagnosis of
orthosomnia) and/or the individual (e.g. if they are concerned they
may have orthosomnia).
[0036] In some examples, the software application is configured to
generate and display, at the user interface, a sleep quality
measure during the sleep analysis or assessment process.
[0037] The step of controlling the software application may
comprise modifying the sleep quality measure in response to the
predictive indicator indicating that a likeliness of orthosomnia in
the individual falls within a first predetermined range.
[0038] The step of controlling the software application may
comprise suppressing the display of the sleep quality measure in
response to the predictive indicator indicating that a likeliness
of orthosomnia falls within a second predetermined range, which may
be different to the first predetermined range (if present).
[0039] The step of controlling the software application may
comprise providing, at the display, supplementary information about
sleep quality measures in response to the predictive indicator
indicating that a likeliness of orthosomnia falls within a third
predetermined range, which may be different to the first and/or
second predetermined range (if present)--but may overlap the first
and/or second predetermined range in other examples.
[0040] Any combination of these approaches could be used. These
embodiments recognize that the information presented by the
software application may affect a likelihood the individual having
orthosomnia. By changing/suppressing/supplementing the information,
a level of control can be exerted over the likelihood of the
individual having orthosomnia, e.g. reducing a likelihood or
intensity of the individual's orthosomnia.
[0041] In some embodiments, the step of obtaining the user
interaction data comprises obtaining, via the user interface,
user-derived sleep quality data representing a subjective
perception of sleep quality of the individual.
[0042] The computer-implemented method may further comprise:
obtaining, from a sleep sensor, sleep sensor data responsive to one
or more changes of physiological parameters of the individual
during the individual's sleep; and processing the sleep sensor data
to generate sensor-derived sleep quality data, being data
representing an objective measure of the sleep quality of the
individual, wherein the supplementary information comprises
information responsive to a difference between the user-derived
sleep quality data and the sensor-derived sleep quality data.
[0043] It is recognized that explanative information that can
explain or justify a difference between an individual's perception
of their sleep, and a software applications prediction of the
quality of their sleep, can help reduce the likelihood and/or
intensity of orthosomnia in the individual. This is because the
user is provided with information that aids them in their
understanding of their clinical state, and therefore reduces the
likelihood of the user developing, or furthering, orthosomnia.
[0044] In some examples, the computer-implemented method is
performed by the software application. Of course, the
computer-implemented method could be performed by a different
software application (e.g. supplementary to the software
application).
[0045] In some examples, the predictive indicator comprise a binary
indicator indicating a prediction of whether or not the individual
has orthosomnia. A binary indicator is a data point having only two
possible value. In other examples, the predictive indicator
comprises a categorical indicator (e.g. data having a discrete or
finite number of possible values), a continuous indicator (e.g.
data having a non-finite number of possible values--or the maximum
number of possible values for a particular computing environment)
or a numerical indicator (e.g. a probability).
[0046] There is also proposed a computer program product comprising
computer program code means which, when executed on a computing
device having a processing system, cause the processing system to
perform all of the steps of any herein described method.
[0047] There is also proposed a processing system for generating a
predictive indicator that indicates a likelihood of orthosomnia in
an individual. The processing system is configured to obtain user
interaction data, being data responsive to the individual's
interactions, via a user interface for a processor, with a software
application running on the processor for performing a sleep
analysis or assessment process; and process the obtained user
interaction data to generate the predictive indicator that
indicates a likelihood of orthosomnia in the individual.
[0048] The skilled person would be readily capable of adapting any
herein described processing system to carry out any herein
described method and vice versa.
[0049] These and other aspects of the invention will be apparent
from and elucidated with reference to the embodiment(s) described
hereinafter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0050] For a better understanding of the invention, and to show
more clearly how it may be carried into effect, reference will now
be made, by way of example only, to the accompanying drawings, in
which:
[0051] FIG. 1 illustrates a processing arrangement;
[0052] FIG. 2 is a flowchart illustrating a method;
[0053] FIG. 3 is a flowchart illustrating a method; and
[0054] FIG. 4 illustrates a processing system.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0055] The invention will be described with reference to the
Figures.
[0056] It should be understood that the detailed description and
specific examples, while indicating exemplary embodiments of the
apparatus, systems and methods, are intended for purposes of
illustration only and are not intended to limit the scope of the
invention. These and other features, aspects, and advantages of the
apparatus, systems and methods of the present invention will become
better understood from the following description, appended claims,
and accompanying drawings. It should be understood that the Figures
are merely schematic and are not drawn to scale. It should also be
understood that the same reference numerals are used throughout the
Figures to indicate the same or similar parts.
[0057] The invention provides a mechanism for identifying
orthosomnia, being the preoccupation with sensor-derived sleep
measures or automated sleep analysis processes, within an
individual. Interactions between the individual and a software
application performing a sleep analysis process are monitored and
used to generate a predictive indicator that indicates a likelihood
that the individual has orthosomnia. This predictive indicator may
be used to control the software application, and in particular, to
control the display of information provided by the software
application.
[0058] Embodiments are based on the realization that interactions
between the individual and the software application, and in
particular the manner in which the individual interacts with the
software application, is indicative of whether or not the
individual has orthosomnia. In this way, a predictive indicator can
be generated to aid in the treatment and/or diagnosis of the
individual, i.e. act as an aid to a clinical decision making
process.
[0059] This proposed concepts aid in the identification, management
and relief of orthosomnia.
[0060] The increasing use of wearable sleep tracking devices allows
individuals to monitor sleep patterns objectively at a consumer
level. However, with access to this objective, albeit often
inaccurate, data--"sensor-derived sleep quality data"--some
individuals can (mistakenly, but unintentionally) identify and
self-diagnose sleep disturbances, for which they proceed to seek
medical treatment. Additionally, individuals may lack the knowledge
to appropriately interpret sensor-derived sleep quality data as
being within a healthy spectrum or as being indicative of a sleep
disorder. For a subset of individuals, the use of sleep trackers
has unintended, adverse effects, as individuals attempt to increase
sleep scores or eliminate undesired disturbances in their data
through methods which often reinforce poor sleep habits, including
spending excessive time in bed (causing a decrease in sleep
efficiency) in order to attempt to increase sleep duration. There
is a recognized condition of orthosomnia, being the preoccupation
with improving or perfecting sleep data. Orthosomnia can result in
a harmful negative feedback loop as an individual's stress about
their sleep causes their sleep quality to further deteriorate.
[0061] This condition was recognized by K. G. Baron et al in the
journal article Baron, K. G., Abbott, S., Jao, N., Manalo, N. and
Mullen, R., 2017. Orthosomnia: Are some patients taking the
quantified self too far?. Journal of Clinical Sleep Medicine,
13(2), pp. 351-354.
[0062] In some cases of orthosomnia, individuals only identify
perceived sleep issues after monitoring sensor-derived sleep
quality data and finding instances of reported restlessness or low
sleep efficiency, of which they were never previously aware, or due
to lack of familiarity with a sleep scoring system. To individuals
having orthosomnia, sensor-derived sleep quality data can feel more
consistent with their experience of sleep than validated
techniques, such as polysomnography or actigraphy. As a result,
individual's perceptions can prove difficult to alter. This can
negatively affect treatment because individuals can be reluctant to
make changes that would decrease or affect their sensor-derived
sleep quality data, such as refusing to try sleep restriction (or
sleep consolidation therapy) because it would temporarily lower
sleep duration. This is compounded by the individual's awareness of
the lack of trust by clinician in sensor-derived sleep quality
data, which affects the confidence that the individual may have in
the clinician.
[0063] This invention is intended to provide approaches for
predicting a likelihood of orthosomnia, and proposes a number of
approaches for managing and relieving orthosomnia in an
individual.
[0064] In particular examples, approaches facilitate improved
understanding (by at least the individual) of the sensor-derived
sleep quality data, placing value on their subjective sleep
experience rather than solely relying on objective reports of
potentially questionable accuracy.
[0065] FIG. 1 illustrates a processing arrangement 100 in which
embodiments may be employed, for the purposes of improved
contextual understanding.
[0066] The processing arrangement 100 comprises a user interface
110, e.g. comprising a display and an input interface. The user
interface 110 is able to communicate with a processor 120. The user
interface 110 and the processor 120 may, as illustrated, be housed
in a same device 105 (e.g. a smartphone, a smartwatch, a laptop and
so on). In other examples, the user interface 110 and the processor
120 are housed separately. For instance, the processor 120 may be a
distributed or cloud-computing system.
[0067] The processor 120 is configured to run, operate or execute a
software application running for performing a sleep analysis or
assessment process. Typically, a sleep analysis process comprises
obtaining biometric data of the individual, e.g. from a sensor 130,
and processing the biometric data to generate sensor-derived sleep
quality data. The sensor-derived sleep quality data represents an
objective measure of the quality of the individual's sleep, such as
a sensor-derived quality measure score (i.e. a "sleep quality
measure"), a total sleep time, a number of sleep interruptions,
length of deep sleep (e.g. non-REM sleep) and so on.
[0068] In some examples, processing the biometric data to generate
the sensor-derived sleep quality data may involve smoothing areas
known to be unreliable according to wearable data or areas known to
be habitually relevant to the individual. For example, if it is
known that the user typically reads for one hour before bed, their
total sleep time (TST) is likely to be overestimated by the
sensor-derived sleep quality data and should be adjusted.
[0069] In some examples, additional information may be used in the
generation of the sensor-derived sleep quality data. The additional
information may, for example, comprise user information, such as an
age, gender, health conditions and diagnosed sleep disorders and/or
typical/historic sleep patterns of the user. The additional
information may comprise information about the sleep sensor and/or
software application (e.g. to take account of any known measuring
errors or inconsistencies) and/or information about the sleep
environment (such as a noise level, light level, etc.).This
additional information may be provided by a user at the user
interface 110.
[0070] Approaches for generating or deriving quality data from
biometric data of an individual (obtained at a sensor 130), and
optionally additional information, are well known, and are not
further described for the sake of conciseness. Approaches may
comprise using a machine-learning algorithm (e.g. a neural network,
a naive Bayesian classifier or a support-vector machine) to
generate the sensor-derived sleep quality data from the biometric
data.
[0071] Some suitable examples of generating sensor-derived sleep
quality data can be found in the US patents having publication
numbers U.S. Ser. Nos. 10/624,574 and 10/524,674, or the US patent
applications having publication numbers U.S. Ser. Nos. 16/495,687
and 16/589,181. Other suitable approaches will be well known.
[0072] The processor 120 may be adapted to control the user
interface 110 to display a visual representation of the
sensor-derived sleep quality data. In particular, the individual
may be able to access or open the software application, and view
the visual representation of the sensor-derived sleep quality
data.
[0073] Thus, the software application is configured so that an
individual is able to access and/or view the sensor-derived sleep
quality data.
[0074] The sensor 130, i.e. a "sleep sensor", is any sensor that is
able to monitor one or more physiological parameters indicative of
a change in a user's quality of sleep. For instance, the
physiological parameter(s) may include one or more of: a heartrate;
a movement; a respiratory rate; brain wave activity and so on. The
sensor 130 may be formed in the same device 105 as the user
interface 110 and/or the processor 120. Preferably, at least the
sensor 130 is wearable by the user, for example, the whole device
105 may be wearable.
[0075] Other suitable configurations for a processing arrangement
100 will be readily understood by the skilled person, e.g.
employing external servers, external processing systems or
communications channels between different aspects of the processing
arrangement.
[0076] FIG. 2 illustrates a computer-implemented method 200
according to an embodiment. The method 200 is configured for
generating a predictive indicator that indicates a likelihood of
orthosomnia in an individual, and may be carried out by a
processing system according to an embodiment. In some examples, the
computer-implemented method is performed by the software
application (executed on the processor 120 of FIG. 1).
[0077] The method 200 comprises a step 210 of obtaining user
interaction data 215. The user interaction data is data responsive
to the individual's interactions, via a user interface for a
processor, with a software application running on the processor for
performing a sleep analysis or assessment process.
[0078] The user interaction data may comprise access data, being
data responsive to the user accessing and/or opening the software
application. The access data may comprise one or more numerical
values responsive to a user accessing and/or opening the software
application, e.g. where a value represents a time of
accessing/opening (e.g. a timestamp or the like), a frequency of
accessing/opening (e.g. how many accesses/openings per
predetermined time period, e.g. per day or in a last predetermined
time window, e.g. a last hour or last two hours), a cumulative
count of accessing/opening, a duration of an access (e.g. in
seconds) and so on.
[0079] In at least one embodiment, the access data comprises time
data indicating a time (of day) at which the individual interacts
with the software application. This may be in the form of one or
more time stamps, or other similar measures. In some examples, the
access data comprises one or more measures of accessing and/or
opening frequency and/or duration of access.
[0080] For the avoidance of doubt, the "access data" may comprise
one or more measures of accessing and/or opening frequency and/or
duration of access of one or more parts of the software application
(e.g. accesses particular screens or data available with the
software application.
[0081] In some examples, the user interaction data comprises
user-derived sleep quality data representing a subjective
perception of sleep quality of the individual, i.e. data that
indicates how the individual feels about their sleep quality
(preferably without being influenced by the sensor-derived sleep
quality data). The user-derived sleep quality data may, for
instance, comprise responses to a survey or a user-provided
indication of perceived quality of sleep (e.g. a numeric indicator
on a predetermined scale). The user-derived sleep quality data is
obtained from a user input provided at the user interface.
[0082] In other examples, the user interaction data comprises one
or more other metrics of an interaction between the individual and
the software application. By way of example only, the user
interaction data may comprise one or more values representing a
number of times the individual views a particular piece of data
(e.g. a particular screen or the sensor-derived sleep quality data,
e.g. in the form of a sleep hypnogram) provided by the software
application, a number of link (e.g. to articles) clicked within the
software application and so on.
[0083] The method 200 then moves to a step 220 of processing the
obtained user interaction data to generate the predictive indicator
that indicates a likelihood of orthosomnia in the individual.
[0084] In the context of the present invention, a predictive
indicator is any data that changes responsive to changes in a
predicted likelihood that the individual has orthosomnia. The
predictive indicator may comprise binary, categorical or numerical
data. Binary data may indicate a prediction as to whether or not
the individual has orthosomnia (e.g. "0" indicates predicted
absence and "1" indicate predicted presence or vice versa).
Categorical data may indicate a likelihood category (e.g. "Likely",
"Very Likely", "Unlikely" and so on) that the individual has
orthosomnia. Numerical data may indicate a numeric probability that
the individual has orthosomnia, e.g. on a scale of 0 to 1 or 0 to
100.
[0085] The precise mechanism for generating the predictive
indicator in step 120 may depend upon the content of the user
interface data.
[0086] In some examples, where the user interaction data comprises
access data, the access data may be compared to one or more
thresholds to determine whether or not the individual has
orthosomnia. The one or more thresholds may, for example, be
derived from population data (e.g. indicating a population average
and/or standard deviation for the access data).
[0087] By way of example, an individual may be considered to have
orthosomnia if they access or open the software application for
more than 1 minute a day and/or more than 5 days a week. As another
example, an individual may be considered to have orthosomnia if
they access or open the software application (e.g. more than a
predetermined number of times, e.g. more than 3 times) during a
predetermined time window (e.g. which may correspond to a time
window when the individual is expected to be asleep).
[0088] As another example, one or more predetermined thresholds may
be set based on population access data. Thus, step 220 may comprise
obtaining population access data, being data that is responsive to
population trends of other individual's interactions with the
software application running on the processor and/or one or more
other versions of the software application running on one or more
other processors; and comparing the access data to the population
access data to generate the predictive indicator.
[0089] The population access data may, for instance, define some
population average and/or standard deviation.
[0090] An individual may be considered to have orthosomnia if their
access data breaches some predetermined threshold with respect to
population access data. For instance, if the access data comprises
a value representing a frequency of access (and the population data
indicates a population mean and/or standard deviation of frequency
of access), the individual may be considered to have orthosomnia if
their frequency of access is more than 2 or 3 standard deviations
greater than the population mean average of frequency.
[0091] In some examples, a value for the predictive indicator may
be set based on a difference between the access data and the
population access data, e.g. a difference between the frequency of
access and the average (population) frequency of access.
[0092] Other predetermined thresholds and/or approaches for setting
the predetermined thresholds could be apparent to the skilled
person. For instance, the predetermined thresholds may be set
according to some clinical protocol and/or in response to one or
more individuals/clinicians' input.
[0093] In some examples, a machine-learning algorithm could be used
to generate the predictive indicator. The machine-learning
algorithm may receive, as input, the user interaction data (e.g.
the access data) and provide, as output, the predictive indicator.
Examples of suitable machine-learning algorithms include a neural
network, a naive Bayesian classifier and a support-vector
machine.
[0094] As one example, the predetermined thresholds may be set
based on historic information or access data of the individual. For
instance, a predetermined threshold may be based upon an average
(mean) and/or standard deviation of values contained in historic
access data, e.g. access data obtained over a previous or past week
or month (although other time periods are also considered).
[0095] More generally, in some examples, the step of processing the
obtained user interaction data comprises: obtaining historic access
data, being data responsive to the individual's historic
interactions, via the user interface, with the software
application; and comparing the access data to the historic access
data to generate the predictive indicator.
[0096] In some embodiments, the access data comprises time data
indicating a time at which the individual interacts with the
software application, e.g. in the form of one or more time stamps.
In these examples, the step of processing the obtained user
interaction data may comprise obtaining biometric data of the user,
the biometric data being responsive to changes in one or more
physiological parameters of the individual at or during a time at
which the individual accesses the and/or opens the software
application, as indicated by the time data.
[0097] The biometric data may then be processed to generate the
predictive indicator, e.g. using predetermined thresholds,
comparisons to population/historic data and/or machine-learning
methods.
[0098] In this way, biometrics associated with the utilization of
the software application are used to detect the probability of
orthosomnia. This embodiment recognizes that a biological response
of the individual can indicate whether or not the user has
orthosomnia (e.g. based on an assessment as to whether they are
nervous/stressed when accessing the software application).
[0099] In particular, biometric data may comprise physiological
data (i.e. measurements) that represent an unconscious or autonomic
response of the individual to information provided by the software
application (e.g. at a user interface). Thus, the physiological
data may be responsive to an autonomic response of the individual
(during the time indicating by the time data).
[0100] Examples include a heartrate (or other heart
characteristics, such as an acceleration of a heartrate), sweating,
a temperature, a respiratory rate, skin color, eye movement and/or
an eye dilation. Other suitable physiological parameters indicative
of an autonomic response and/or nervousness/stress of an individual
will be apparent to the skilled person.
[0101] The biometric data may be compared to one or more
thresholds, e.g. set by a clinician or based on population data, to
generate the predictive indicator. The threshold may be defined to
identify an autonomic response indicating nervousness or stress
when accessing the software application (herein identified as being
a sign/symptom of orthosomnia). For instance, if the biometric data
comprises a measured heartrate, the predictive indicator may
indicate that the individual is likely to have orthosomnia if their
measured heartrate exceeds some predetermined threshold when
accessing the software application. As another example, if the
biometric data comprises an eye dilation, the predictive indicator
may indicate that the individual is likely to have orthosomnia if
their measured eye dilation (e.g. detectable with a camera or the
like) exceeds some predetermined threshold.
[0102] The biometric data may be compared to historic biometric
data. For instance, if the biometric data comprises a measured
respiratory rate, an increase in respiratory rate when the user
access the software application may indicate a likelihood of
orthosomnia (as this may indicate nervousness about the results of
the sleep analysis/assessment).
[0103] The biometric data may be processed using a machine-learning
method configured to generate the predictive indicator. Examples of
machine-learning methods have been previously described.
[0104] In yet another embodiment, usable when the user interaction
data comprises user-derived sleep quality data, the user-derived
sleep quality data may be compared to sensor-derived sleep quality
data to generate the predictive indicator. In this way, generating
the predictive indicator may include an assessment of the mismatch
between a subjective perception of sleep quality (the user-derived
sleep quality data) and objective sleep tracker data (the
sensor-derived sleep quality data).
[0105] The user-derived sleep quality data may, for example,
comprise one or more a numerical measure or indicator of a
perceived quality of sleep provided by the individual (e.g. on a
scale of 0 to 10, 1 to 10, 0 to 100 or 1 to 100). The user-derived
sleep quality data may comprise a plurality of such
measures/indicators, e.g. representing perceived quality for
different aspects of sleep (e.g. perceived sleep depth, sleep
consistency, wakefulness and so on).
[0106] Thus, as illustrated, step 220 may comprise a step 221 of
obtaining, from a sleep sensor, sleep sensor data 225 responsive to
one or more changes of physiological parameters of the individual
during the individual's sleep. The sleep sensor data may, for
instance, comprise biometric data of the individual.
[0107] The sleep sensor may be external to the processing system
performing the method 200, or may be housed in a same device as the
processing system.
[0108] The step 220 may also comprise a step 222 of processing the
sleep sensor data to generate sensor-derived sleep quality data,
being data representing an objective measure of the sleep quality
of the individual. Preferably, the sensor-derived sleep quality
data provides sleep sensor derived predictions of the same values
or measures (e.g. on a same scale) contained in the user-derived
sleep quality data. Mechanisms for generating sensor-derived sleep
quality data have been previously described with reference to FIG.
1.
[0109] The step 220 may then perform a step 223 of comparing the
user-derived quality input data to the sensor-derived sleep quality
data to generate the predictive indicator.
[0110] The user-derived sleep quality data and the sensor-derived
sleep quality data may be normalized with respect to one another.
For instance, both types of quality data may include a measure of
sleep quality on a same scale for the purposes of comparison. The
predictive indicator may be responsive to a difference in the
measure of sleep quality.
[0111] Step 223 may comprise determining a difference between the
user-derived quality input data and the sensor-derived quality
input data, and processing this difference to generate the
predictive indicator. This may include processing the difference
using predetermined thresholds, comparisons to population/historic
data and/or machine-learning methods.
[0112] If possible, information used to determine the user-derived
sleep quality data should be collected before the individual sees
(e.g. at the user interface) any sensor-derived sleep quality data
(e.g. graph, sleep score, etc.). This is because the sleep
perception of indiviudals with orthosomnia may be affected by their
objective sleep data, which would affect the reliabiltiy of the
user-derived sleep quality data.
[0113] Proposed embodiments thereby facilitate generation of a
predictive indicator responsive to user interaction data which
represents information obtained from a user's interaction with a
software application (for sleep analysis/assessment). The user
interaction data may comprise information intentionally supplied by
the user (e.g. usable to generate user-derived sleep quality data)
and/or metadata concerning a user's interaction with the software
application (e.g. information about a time, frequency and/or
duration of access).
[0114] A combination of the above described approaches used for
generating the predictive indicator may be employed.
[0115] In some embodiments, a combination of user interaction data
and data derived from a sleep sensor ("sleep sensor data") may be
used to generate the predictive indicator. In other words, the
predictive indicator may be responsive to a combination of user
interaction data and sensor-derived data.
[0116] For instance, user interaction data may comprise a frequency
with which the individual access the software application (or
certain parts of the software application), and the sleep sensor
data may comprise a predicted length of sleep (or time in
bed)--e.g. by assessing movement of the individual. The predictive
indicator may indicate likely orthosomnia if the user interaction
data indicates an increased frequency of checking the software
application (or certain parts thereof) and an increased level of
sleep (e.g. attempts to obtain 9 or 10 hours of sleep). This
indicates that the user is attempting to get more time asleep, and
may therefore have orthosomnia.
[0117] Other suitable examples will be apparent to the skilled
person, e.g. if the sleep sensor data indicates increased
restlessness (during sleep) accompanied by frequent access of the
software application during the night (e.g. more than 3 times
during the night), indicating that the individual is stressed about
their sleep during the night.
[0118] In some examples, the computer-implemented method further
comprises a step (not shown) of displaying the predictive indictor
via the user interface. This provides an individual/clinician with
additional information to aid them in performing a
treatment/diagnostic process.
[0119] This disclosure also proposes mechanisms to perform in
response to the predictive indicator indicating that the user has,
or is likely to have, orthosomnia. In particular, the disclosure
proposes an approach for controlling the software application to
modify and/or supplement information presented to the individual,
via a user interface, during the sleep analysis or assessment
process based on the predictive indicator.
[0120] The purpose of these mechanisms is the reduction of an
individual's stress and anxiety related to sleep scores and the
desire to achieve perceived perfect sleep.
[0121] Thus, there is proposed a computer-implemented method of
controlling a software application running on a processor for
performing a sleep analysis or assessment process.
[0122] Various approaches for controlling the software application
are envisaged in the present disclosure if the individual is
suspected of orthosomnia, e.g. if the predictive indicator
indicators that the user is likely to have orthosomnia. These
approaches are particularly useful if the software application is
configured to generate and display, at the user interface, a sleep
quality measure--which is derived from data obtained from a sleep
sensor (i.e. is a sensor-derived sleep quality measure).
[0123] FIG. 3 illustrates a flowchart usable for describing various
embodiments of such a computer-implemented method 300. The method
300 comprises performing a process 200 of generating a predictive
indicator that indicates a likelihood of orthosomnia in an
individual. Example processes 200 have been previously described
with reference to FIG. 2.
[0124] In one example, the method 300 comprises a step 310 of
controlling the software application to modify the sleep quality
measure (displayed at the user interface) in response to the
predictive indicator indicating that a likeliness of orthosomnia in
the individual falls within a first predetermined range.
[0125] Thus, instead of simply displaying a sleep quality measure
as derived from sensor data, the sleep quality measure may be
revised to take account of likely orthosomnia of the individual,
e.g. making the sleep quality measure more positive (e.g. revising
a sleep quality measure upwards) to reduce anxiety or stress on the
individual. This step effectively aims to obfuscate the sleep
quality measure based on the sleep tracker analysis algorithm, to
reduce the impact of the sleep quality measure data on the
individual.
[0126] In another example, the method 300 comprises a step 320 of
suppressing the display of the sleep quality measure in response to
the predictive indicator indicating that a likeliness of
orthosomnia falls within a second predetermined range. Thus, a
sleep quality measure may prevent a sleep quality measure from
being generated and presented to the individual.
[0127] The display of the sleep quality measure may, for instance,
be suppressed for a predetermined period of time (e.g. 3 days or a
week) and/or during certain time period or times of the day (e.g.
during a time during which the individual is supposed to be
asleep).
[0128] This approach aims to address an individual's obsession with
the sensor-derived sleep quality data, to reduce anxiety and/or
stress about the sensor-derived sleep quality measure if they are
predicted to have orthosomnia. In particular, this approach is
intended to encourage the individual to avoid behavioral habits
that may lead to, or reinforce existing, orthosomnia (i.e. frequent
software application checking and the like).
[0129] In some examples, the step of suppressing the display of the
sleep quality measure may comprise removing access to, or
preventing the individual from accessing, the software application,
e.g. for a predetermined period of time (e.g. 3 days or a week)
and/or during certain time periods (e.g. during the night).
[0130] In some examples, the method 300 comprises a step 330 of
(generating and) providing, at the display, supplementary
information about sleep quality measures in response to the
predictive indicator indicating that a likeliness of orthosomnia
falls within a third predetermined range.
[0131] The purpose of the supplementary information may be to
divert the individual's attention from a sleep quality measure
and/or decatastrophize a poor sleep quality measure.
[0132] In particularly preferable examples, the supplementary
information is responsive to a difference between user-derived
sleep quality data and the sensor-derived sleep quality data. This
approach can help emphasize potential discrepancies between the
user's reported experiences and suggesting plausible explanations
for such differences.
[0133] For instance, if a difference between user-derived sleep
quality data and the sensor-derived sleep quality data is
identified, the supplementary information may generate text
indicating that the sensor-derived data may be inaccurate (e.g. as
it differs significantly from the user-derived data). In
particular, orthosomnia can relate to a scenario in which the user
has a subjective feeling of high sleep quality, but the
sensor-derived sleep quality data indicates that the user had a low
sleep quality. The supplementary information may provide plausible
explanations for this inaccuracy (e.g. incorrect placement of the
sleep sensor, over or under-sensitivity of the sleep sensor and so
on). This approach can help assuage an individual's concerns,
whilst maintaining their confidence in the method of the present
disclosure.
[0134] Accordingly, step 330 may comprise a step 331 of obtaining,
from a sleep sensor, sleep sensor data 225 responsive to one or
more changes of physiological parameters of the individual during
the individual's sleep; and a step 332 of processing the sleep
sensor data to generate sensor-derived sleep quality data, being
data representing an objective measure of the sleep quality of the
individual; and a step 333 of generating the supplementary
information, which comprises information responsive to a difference
between the user-derived sleep quality data 215 (e.g. obtained by
process 200) and the sensor-derived sleep quality data. Steps 331
and 332 may be functionally equivalent to steps 221 and 222
(described with reference to FIG. 2), and may be the same process
(i.e. the process is only performed once).
[0135] In some examples, the supplementary information may contain
information specific to the software application performing the
sleep analysis and/or assessment and/or the sleep
analysis/assessment approach itself, such as a confidence level in
the sleep quality measure (e.g. in terms of %). This aids the
individual in gaining confidence in the system and algorithm.
[0136] In some examples, the supplementary information is
responsive to historic and/or global sleep trends. This can, for
instance, aid in contextualizing, decatastrophzing and/or otherwise
justifying a poor night's sleep.
[0137] The supplementary information may thereby provide contextual
information offering global normative information and/or
information on the users' past history.
[0138] For instance, one example of supplementary information may
be text indicating "You've had two nights of poor quality sleep
this week. While we all want to sleep well every night, sleep
specialists don't typically diagnose a problem unless you have
difficulty falling or staying asleep typically three or more nights
per week. Your history suggests that you typically have healthy
sleep patterns and weeks like this aren't out of the ordinary. In
fact, 95% of healthy normal sleepers have had weeks just like this
or worse!". It will be appreciated that the content of this
exemplary text may be revised depending upon the specific
circumstances for an individual--for instance, text may be omitted
if irrelevant and/or values in the example text may be modified to
reflect a true user's situation.
[0139] The supplementary information may also/otherwise provide
text seeking to decatastrophize an individual for cognitive
restructuring around the potential impact of a poor night of sleep
includes offering user encouragement. Some example text may
indicate "While last night's sleep wasn't the best, last time this
happened you bounced back quickly.". The skilled person will
appreciate how the supplementary information may therefore depend
upon historic data of the individual.
[0140] Supplementary information thereby provides the opportunity
for the individual to reflect on the likely magnitude and type of
impact of the poor night of sleep is on their day.
[0141] The approaches described above make use of predetermined
ranges with respect to the predictive indicator. The nature of the
predetermined ranges may depend upon the content of the predictive
indicator, and can include binary ranges, categorical ranges and/or
numeric ranges as appropriate. The predetermined ranges may be
identical and/or may overlap one another.
[0142] FIG. 4 is a schematic diagram of a processing system 400,
according to embodiments of the present disclosure. As shown, the
processing system 400 may include a (data) processor 460, a memory
464, and a communication module 468. These elements may be in
direct or indirect communication with each other, for example via
one or more buses.
[0143] The processor 460 may include a central processing unit
(CPU), a digital signal processor (DSP), an ASIC, a controller, an
FPGA, another hardware device, a firmware device, or any
combination thereof configured to perform the operations described
herein. The processor 460 may also be implemented as a combination
of computing devices, e.g., a combination of a DSP and a
microprocessor, a plurality of microprocessors, one or more
microprocessors in conjunction with a DSP core, or any other such
configuration. In some embodiments, the processor is a distributed
processing system, e.g. formed of a set of distributed
processors.
[0144] The memory 464 may include a cache memory (e.g., a cache
memory of the processor 460), random access memory (RAM),
magnetoresistive RAM (MRAM), read-only memory (ROM), programmable
read-only memory (PROM), erasable programmable read only memory
(EPROM), electrically erasable programmable read only memory
(EEPROM), flash memory, solid state memory device, hard disk
drives, other forms of volatile and non-volatile memory, or a
combination of different types of memory. In an embodiment, the
memory 464 includes a non-transitory computer-readable medium. The
non-transitory computer-readable medium may store instructions. For
example, the memory 464, or non-transitory computer-readable medium
may have program code recorded thereon, the program code including
instructions for causing the processing system 400, or one or more
components of the processing system 400, particularly the processor
460, to perform the operations described herein. For example, the
processing system 400 can execute operations of the method 700.
Instructions 466 may also be referred to as code or program code.
The terms "instructions" and "code" should be interpreted broadly
to include any type of computer-readable statement(s). For example,
the terms "instructions" and "code" may refer to one or more
programs, routines, sub-routines, functions, procedures, etc.
"Instructions" and "code" may include a single computer-readable
statement or many computer-readable statements. The memory 464,
with the code recorded thereon, may be referred to as a computer
program product.
[0145] The communication module 468 can include any electronic
circuitry and/or logic circuitry to facilitate direct or indirect
communication of data between the processing system 400, the
penetration device and/or the user interface (or other further
device). In that regard, the communication module 468 can be an
input/output (I/O) device. In some instances, the communication
module 468 facilitates direct or indirect communication between
various elements of the processing circuit 400 and/or the
arrangement (FIG. 1).
[0146] It will be understood that disclosed methods are preferably
computer-implemented methods. As such, there is also proposed the
concept of a computer program comprising computer program code for
implementing any described method when said program is run on a
processing system, such as a computer or a set of distributed
processors.
[0147] Different portions, lines or blocks of code of a computer
program according to an embodiment may be executed by a processing
system or computer to perform any herein described method. In some
alternative implementations, the functions noted in the block
diagram(s) or flow chart(s) may occur out of the order noted in the
figures. For example, two blocks shown in succession may, in fact,
be executed substantially concurrently, or the blocks may sometimes
be executed in the reverse order, depending upon the functionality
involved.
[0148] The present disclosure proposes a computer program (product)
comprising instructions which, when the program is executed by a
computer or processing system, cause the computer or processing
system to carry out (the steps of) any herein described method. The
computer program (product) may be stored on a non-transitory
computer readable medium.
[0149] Similarly, there is also proposed a computer-readable
(storage) medium comprising instructions which, when executed by a
computer or processing system, cause the computer or processing
system to carry out (the steps of) any herein described method.
There is also proposed computer-readable data carrier having stored
thereon the computer program (product) previously described. There
is also proposed a data carrier signal carrying the computer
program (product) previously described.
[0150] Variations to the disclosed embodiments can be understood
and effected by those skilled in the art in practicing the claimed
invention, from a study of the drawings, the disclosure and the
appended claims. In the claims, the word "comprising" does not
exclude other elements or steps, and the indefinite article "a" or
"an" does not exclude a plurality. A single processor or other unit
may fulfill the functions of several items recited in the claims.
The mere fact that certain measures are recited in mutually
different dependent claims does not indicate that a combination of
these measures cannot be used to advantage. A computer program may
be stored/distributed on a suitable medium, such as an optical
storage medium or a solid-state medium supplied together with or as
part of other hardware, but may also be distributed in other forms,
such as via the Internet or other wired or wireless
telecommunication systems. If the term "adapted to" is used in the
claims or description, it is noted the term "adapted to" is
intended to be equivalent to the term "configured to". Any
reference signs in the claims should not be construed as limiting
the scope.
* * * * *