U.S. patent application number 17/229654 was filed with the patent office on 2022-01-20 for health monitoring and guidance.
This patent application is currently assigned to Livmor, Inc.. The applicant listed for this patent is Livmor, Inc.. Invention is credited to Ross Grady Baker, JR., Ken Persen.
Application Number | 20220015653 17/229654 |
Document ID | / |
Family ID | |
Filed Date | 2022-01-20 |
United States Patent
Application |
20220015653 |
Kind Code |
A1 |
Persen; Ken ; et
al. |
January 20, 2022 |
HEALTH MONITORING AND GUIDANCE
Abstract
A photoplethysmographic (PPG) signal communicated by a PPG
sensor of a wearable device worn by a user may be received by a
processor. The processor may detect a plurality of heartbeats of
the user from the PPG-signal, determine a heart rate of the user
based on at least the plurality of heartbeats, determine a heart
rate variability (HRV) based on the plurality of heartbeats,
determine a respiration rate of the user based on a low frequency
component of the PPG signal, and determine whether the user is in a
stressed state based on the heart rate, the HRV, and the
respiration rate. The processor may cause the display of
information related to the stress state of the user, and
instructions and/or advice for reducing a stress level of the
user.
Inventors: |
Persen; Ken; (Dove Canyon,
CA) ; Baker, JR.; Ross Grady; (Bellaire, TX) |
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Applicant: |
Name |
City |
State |
Country |
Type |
Livmor, Inc. |
Irvine |
CA |
US |
|
|
Assignee: |
Livmor, Inc.
Irvine
CA
|
Appl. No.: |
17/229654 |
Filed: |
April 13, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16362527 |
Mar 22, 2019 |
10980433 |
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17229654 |
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16035568 |
Jul 13, 2018 |
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16362527 |
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62535391 |
Jul 21, 2017 |
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International
Class: |
A61B 5/024 20060101
A61B005/024; A61B 5/00 20060101 A61B005/00; A61B 5/11 20060101
A61B005/11 |
Claims
1. A computer program product comprising a non-transitory,
machine-readable medium storing instructions which, when executed
by at least one programmable processor, cause the at least one
programmable processor to perform operations comprising: receiving,
at the at least one programmable processor, a photoplethysmographic
(PPG) signal communicated by a PPG sensor of a wearable device worn
by a user; detecting a plurality of heartbeats of the user based on
the PPG-signal; determining a heart rate of the user based on at
least the plurality of heartbeats; determining a heart rate
variability (HRV) of the user based on the plurality of heartbeats;
determining a respiration rate of the user based on a low frequency
component of the PPG signal; and determining whether the user is in
a stressed state based on the heart rate, the HRV, and the
respiration rate, wherein determining whether the user is in a
stressed state based on the heart rate, the HRV, and the
respiration rate comprises determining a mathematical model for the
user, causing the heart rate, the HRV, and the respiration rate to
be used as inputs into the mathematical model, and causing the
mathematical model to output the determination of whether the user
is in a stressed state based on the inputs, wherein the
mathematical model comprises a weighted combination of the heart
rate, the respiration rate, and the HRV for the user, wherein the
heart rate is weighted more heavily than the respiration rate or
the HRV.
2. The computer program product of claim 1, wherein the detecting
the plurality of heartbeats comprises detecting the plurality of
heartbeats of the user from a maximum gradient of the PPG
signal.
3. The computer program product of claim 2, wherein detecting the
plurality of heartbeats further comprises performing spline
interpolation on local maxima of the maximum gradient.
4. The computer program product of claim 1, wherein determining
whether the user is in a stressed state based on the heart rate,
the HRV, and the respiration rate comprises determining a stress
level of the user based on the heart rate, the HRV, and the
respiration rate.
5. The computer program product of claim 1, wherein the weighted
combination of heart rate, respiration rate, and heart rate
variability comprises a three feature vector, and causing the
mathematical model to output the determination of whether the user
is in a stressed state comprises analyzing the three feature vector
in a three dimensional vector space to determine whether the three
feature vector breaches one or more thresholds that define one or
more stress zones or volumes in the three dimensional vector space,
the mathematical model configured such that, responsive to at least
a portion of the three feature vector passing through and/or being
bounded by the one or more stress zones or volumes, the
mathematical model determines that the user is in a stressed
state.
6. The computer program product of claim 5, wherein the thresholds
are determined based on user profile information, the user profile
information describing baseline or normal pathological values for
the heart rate, the respiration rate, and the HRV of the user.
7. The computer program product of claim 1, wherein the operations
performed by the at least one programmable processor further
comprise receiving, at the at least one programmable processor, a
motion signal communicated by an accelerometer sensor of the
wearable device worn by the user, the motion signal conveying
information related to an activity level of the user; and, wherein
determining whether the user is in a stressed state is further
based on the activity level.
8. The computer program product of claim 7, wherein determining
whether the user is in a stressed state includes a determination of
whether the activity level of the user breaches a minimum activity
threshold level.
9. The computer program product of claim 1, wherein the operations
performed by the at least one programmable processor further
comprise causing display of information related to the
determination of whether the user is in a stressed state on a
display of the wearable device and/or a different computing device
associated with the user.
10. The computer program product of claim 9, wherein the operations
performed by the at least one programmable processor further
comprise making multiple determinations of whether the user is in a
stressed state over a period of time, and wherein the causing
display of information related to the determination of whether the
user is in a stressed state comprises causing display of
information related to the multiple determinations of whether the
user is in a stressed state.
11. The computer program product of claim 10, wherein the
operations performed by the at least one programmable processor
further comprise determining an amount of time the user is in the
stressed state during the period of time.
12. The computer program product of claim 1, wherein the operations
performed by the at least one programmable processor further
comprise receiving, at the at least one programmable processor, a
motion signal communicated by an accelerometer sensor of the
wearable device worn by a user during a period of time, the motion
signal conveying information related to an activity level of the
user.
13. The computer program product of claim 12, wherein the
operations performed by the at least one programmable processor
further comprise: determining whether the activity level is
indicative of sleep, exercise, and/or normal daily activity; and
causing display of periods of stress, sleep, exercise, and/or
normal daily activity during the period of time.
14. The computer program product of claim 1, wherein the operations
performed by the at least one programmable processor further
comprise, responsive to a determination that the user is in a
stressed state, determining breathing guidance and causing display
of the breathing guidance to the user on a display of the wearable
device to facilitate stress reduction.
15. The computer program product of claim 11, wherein the
operations performed by the at least one programmable processor
further comprise generating and causing display of a recommendation
for lifestyle changes determined based on the amount of time the
user is in the stressed state.
16. A wrist worn device configured to determine whether a user is
in a stressed state, the wrist worn device comprising: a
photoplethysmographic (PPG) sensor configured to generate a PPG
signal; at least one programmable processor and a non-transitory,
machine-readable medium storing instructions which, when executed
by the at least one programmable processor, cause the at least one
programmable processor to perform operations comprising: receiving,
at the at least one programmable processor, the PPG signal
communicated by the PPG sensor; detecting a plurality of heartbeats
of the user based on the PPG-signal; determining a heart rate of
the user based on at least the plurality of heartbeats; determining
a heart rate variability (HRV) of the user based on the plurality
of heartbeats; determining a respiration rate of the user based on
a low frequency component of the PPG signal; and determining
whether the user is in a stressed state based on the heart rate,
the HRV, and the respiration rate, wherein determining whether the
user is in a stressed state based on the heart rate, the HRV, and
the respiration rate comprises determining a mathematical model for
the user, causing the heart rate, the HRV, and the respiration rate
to be used as inputs into the mathematical model, and causing the
mathematical model to output the determination of whether the user
is in a stressed state based on the inputs, wherein the
mathematical model comprises a weighted combination of the heart
rate, the respiration rate, and the HRV for the user, wherein the
heart rate is weighted more heavily than the respiration rate or
the HRV; and a user interface controlled by the at least one
programmable processor, the user interface controlled by the at
least one programmable processor to display information related to
the determination of whether the user is in a stressed state.
17. The wrist worn device of claim 16, wherein the detecting the
plurality of heartbeats comprises detecting the plurality of
heartbeats of the user from a maximum gradient of the PPG
signal.
18. The wrist worn device of claim 17, wherein detecting the
plurality of heartbeats further comprises performing spline
interpolation on local maxima of the maximum gradient.
19. The wrist worn device of claim 16, wherein determining whether
the user is in a stressed state based on the heart rate, the HRV,
and the respiration rate comprises determining a stress level of
the user based on the heart rate, the HRV, and the respiration
rate.
20. The wrist worn device of claim 18, wherein the weighted
combination of heart rate, respiration rate, and heart rate
variability comprises a three feature vector, and causing the
mathematical model to output the determination of whether the user
is in a stressed state comprises analyzing the three feature vector
in a three dimensional vector space to determine whether the three
feature vector breaches one or more thresholds that define one or
more stress zones or volumes in the three dimensional vector space,
the mathematical model configured such that, responsive to at least
a portion of the three feature vector passing through and/or being
bounded by the one or more stress zones or volumes, the
mathematical model determines that the user is in a stressed
state.
21-34. (canceled)
Description
RELATED APPLICATION(S)
[0001] This application is a continuation of and claims priority to
and the benefit of U.S. patent application Ser. No. 16/362,527,
filed Mar. 22, 2019, titled "Health Monitoring and Guidance," which
claims priority to U.S. patent application Ser. No. 16/035,568,
filed Jul. 13, 2018, titled "Systems and Methods for Health
Monitoring and Guidance," which claims priority to U.S. Provisional
Patent Application No. 62/535,391, filed Jul. 21, 2017, the
disclosures of which are hereby incorporated by reference in their
entirety.
DESCRIPTION OF THE RELATED ART
[0002] Disclosures herein relate to monitoring the health of a user
and providing health guidance. For example, electrical and
physiological characteristics of a user's human heart can be
measured using sensors such as electrocardiogram (ECG) sensors or
photoplethysmograph (PPG) sensors. In some circumstances, such
sensors may be included on a wearable device such as a smartwatch.
Signals from such sensors may then be analyzed to determine useful
and informative health states of a patient, such as heart rates,
heart rate variability, particular heart rhythms, and the like.
SUMMARY
[0003] Systems, methods and computer software for monitoring the
heath of a user are disclosed herein. In one implementation, a
computer program product is described comprising a non-transitory,
machine-readable medium storing instructions which, when executed
by a processor, may cause the processor to perform operations such
as receiving a photoplethysmographic (PPG) signal communicated by a
PPG sensor of a wearable device worn by a user, detecting a
plurality of heartbeats of the user based on the PPG-signal,
determining a heart rate of the user based on at least the
plurality of heartbeats, determining a heart rate variability (HRV)
of the user based on the plurality of heartbeats, determining a
respiration rate of the user based on a low frequency component of
the PPG signal, and determining whether the user is in a stressed
state based on the heart rate, the HRV, and the respiration
rate.
[0004] In some implementations, determining whether the user is in
a stressed state based on the heart rate, the HRV, and the
respiration rate may comprise determining a mathematical model for
the user, causing the heart rate, the HRV, and the respiration rate
to be used as inputs into the mathematical model, and causing the
mathematical model to output the determination of whether the user
is in a stressed state based on the inputs. In some
implementations, the mathematical model may comprise a weighted
combination of the heart rate, the respiration rate, and the HRV
for the user. The heart rate may be weighted more heavily than the
respiration rate or the HRV.
[0005] In certain implementations, the weighted combination of
heart rate, respiration rate, and heart rate variability may
comprise a three feature vector and causing the mathematical model
to output the determination of whether the user is in a stressed
state may comprise analyzing the three feature vector in a three
dimensional vector space to determine whether the three feature
vector breaches one or more thresholds that define one or more
stress zones or volumes in the three dimensional vector space. The
mathematical model may be configured such that, responsive to at
least a portion of the three feature vector passing through and/or
being bounded by the one or more stress zones or volumes, the
mathematical model determines that the user is in a stressed
state.
[0006] In some implementations, the operations performed by the
programmable processor may further comprise causing the display of
information related to the determination of whether the user is in
a stressed state on a display of a wearable device and/or on a
different computing device associated with the user. In some
implementations, the operations may further include making multiple
determinations of whether the user is in a stressed state over a
period of time.
[0007] In additional implementations, the operations may further
include receiving a motion signal communicated by an accelerometer
on the wearable device worn by a user during a period of time. The
motion signal may convey information related to an activity level
of the user. In some implementations, the operations performed by
the programmable processor may further including determining
whether the activity level is indicative of sleep, exercise, and/or
normal daily activity; and causing the display of periods of
stress, sleep, exercise, and/or normal daily activity for the
period of time.
[0008] In additional implementations, the operations performed by
the programmable processor may further comprise (responsive to a
determination that the user is in a stressed state) determining
breathing guidance and causing display of the breathing guidance to
the user on a display of the wearable device, to facilitate stress
reduction.
[0009] As another example, a wrist worn device configured to
determine whether a user is in a stressed state is described. The
wrist worn device may comprise a photoplethysmographic (PPG)
sensor, at least one programmable processor and a non-transitory
storage medium, a user interface, and/or other components. The PPG
sensor may be configured to generate a PPG signal. The
non-transitory, machine-readable medium may store instructions
which, when executed by the at least one programmable processor,
cause the programmable processor to perform operations comprising:
receiving, at the at least one programmable processor, the PPG
signal communicated by the PPG sensor, detecting a plurality of
heartbeats of the user based on the PPG-signal, determining a heart
rate of the user based on at least the plurality of heartbeats,
determining a heart rate variability (HRV) of the user based on the
plurality of heartbeats, determining a respiration rate of the user
based on a low frequency component of the PPG signal, and
determining whether the user is in a stressed state based on the
heart rate, the HRV, and the respiration rate. The user interface
may be controlled by the programmable processor to display
information related to the determination of whether the user is in
a stressed state.
[0010] Implementations of the current subject matter can include,
but are not limited to, methods consistent with the descriptions
provided herein as well as articles that comprise a tangibly
embodied machine-readable medium operable to cause one or more
machines (e.g., computers, etc.) to result in operations
implementing one or more of the described features. Similarly,
computer systems are also contemplated that may include one or more
processors and one or more memories coupled to the one or more
processors. A memory, which can include a computer-readable storage
medium, may include, encode, store, or the like, one or more
programs that cause one or more processors to perform one or more
of the operations described herein. Computer implemented methods
consistent with one or more implementations of the current subject
matter can be implemented by one or more data processors residing
in a single computing system or across multiple computing systems.
Such multiple computing systems can be connected and can exchange
data and/or commands or other instructions or the like via one or
more connections, including but not limited to a connection over a
network (e.g., the internet, a wireless wide area network, a local
area network, a wide area network, a wired network, or the like),
via a direct connection between one or more of the multiple
computing systems, etc.
[0011] The details of one or more variations of the subject matter
described herein are set forth in the accompanying drawings and the
description below. Other features and advantages of the subject
matter described herein will be apparent from the description and
drawings, and from the claims. While certain features of the
currently disclosed subject matter are described for illustrative
purposes in relation to particular implementations, it should be
readily understood that such features are not intended to be
limiting. The claims that follow this disclosure are intended to
define the scope of the protected subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The accompanying drawings, which are incorporated in and
constitute a part of this specification, show certain aspects of
the subject matter disclosed herein and, together with the
description, help explain some of the principles associated with
the disclosed implementations. In the drawings,
[0013] FIG. 1 illustrates an exemplary system that can provide for
the monitoring of user health characteristics and provide
health-related guidance in accordance with certain aspects of the
present disclosure,
[0014] FIG. 2 illustrates an implementation of a user wearable
device in accordance with certain aspects of the present
disclosure,
[0015] FIG. 3 illustrates an implementation of a communication
device in accordance with certain aspects of the present
disclosure,
[0016] FIG. 4A illustrates example heartbeat interval data in
accordance with certain aspects of the present disclosure,
[0017] FIG. 4B illustrates an example method for heartbeat
detection in accordance with certain aspects of the present
disclosure,
[0018] FIG. 4C illustrates an example of spline interpolation in
accordance with certain aspects of the present disclosure,
[0019] FIG. 5 illustrates an exemplary display of a user's health
states over time, utilizing HRV calculations in accordance with
certain aspects of the present disclosure,
[0020] FIG. 6A illustrates a first example of a health information
display of a user wearable device application and/or a
communication device application in accordance with certain aspects
of the present disclosure,
[0021] FIG. 6B illustrates a second example of a health information
display of a user wearable device application and/or a
communication device application in accordance with certain aspects
of the present disclosure,
[0022] FIG. 6C illustrates a third example of a health information
display of a user wearable device application and/or a
communication device application in accordance with certain aspects
of the present disclosure,
[0023] FIG. 6D illustrates a fourth example of a health information
display of a user wearable device application and/or a
communication device application in accordance with certain aspects
of the present disclosure,
[0024] FIG. 6E illustrates a fifth example of a health information
display of a user wearable device application and/or a
communication device application in accordance with certain aspects
of the present disclosure, and
[0025] FIG. 7 illustrates an exemplary display that may be used to
guide a user's breathing rate in accordance with certain aspects of
the present disclosure.
DETAILED DESCRIPTION
[0026] The subject matter described herein relates to systems,
methods and software for monitoring certain heath states of a user
and for providing the user with guidance. Exemplary systems are
described that can utilize information from sensors to assess
various aspects of user's health, for example, by analyzing
relationships between heart rate, respiration rate, heart rate
(and/or pulse rate) variability, and/or other parameters.
[0027] The present systems can be configured to determine levels of
stress experienced by a user and to provide guidance that may
facilitate a reduction in the user's level of stress. For example,
if a user observes (or is alerted to) the occurrence of prolonged
periods of stress, the user can be provided with breathing
exercises to reduce stress--potentially preventing deterioration of
the user's health. In some implementations, the present systems can
be configured to monitor respiration rate, heart rate, HRV/PRV
and/or other parameters to assess the effectiveness of the
breathing exercises and to present feedback to the user so he or
she can see the immediate effect of the exercises on his or her
current level of stress and physiology. This can provide a positive
feedback mechanism that may encourage the user to continue the
exercises as they can directly and quantifiably see the effect on
their physiology.
[0028] FIG. 1 illustrates an exemplary system 100 that can provide
for the monitoring of health characteristics of a user (for
example, a human patient, or other living organism) and can provide
health guidance to the user based on the health characteristics
monitoring.
[0029] In some implementations, the exemplary system 100 depicted
in FIG. 1 may include elements such as user wearable device(s) 108
(e.g., a smartwatch), communication devices 102, 104, and 106
(e.g., a mobile phone or PC), user monitoring devices 110 and 112
(e.g., a separate smart scale or blood glucose monitor), data
analysis device(s) 114, server(s) 116 (e.g., including processor(s)
117 and database(s) 118), network(s) 120, and/or other components.
The server(s) 116, wearable devices 108, communication devices
102-106, user monitoring devices 110 and 112, data analytics
devices 114 and/or other devices may include communication lines or
ports to enable the exchange of information within a network (e.g.,
network 120), or within other computing platforms via wired or
wireless techniques (e.g., Ethernet, fiber optics, coaxial cable,
WiFi, Bluetooth, near field communication, or other
technologies).
[0030] It should be noted that, while one or more operations are
described herein as being performed by particular components of
system 100, those operations may, in some implementations, be
performed by other components of system 100. As an example, while
one or more operations are described herein as being performed by
components of data analysis device(s) 114, those operations may, in
other implementations, be performed by components of the user
wearable device(s) 108, by components of the communications devices
102, 104, and 106, and/or by other components of system 100. In
addition, although many of the devices are shown separately, one or
more components shown in FIG. 1 may be included in and/or coupled
to one more other components shown in FIG. 1. For example, one or
more data analysis devices 114 may be included in one or more
servers 116, one more communication devices 102-106, one or more
user monitoring devices 110 and/or 112, one or more user wearable
devices 108, and/or other components.
[0031] The user wearable device(s) 108 may be a smartwatch (for
example, Samsung Gear, Apple Watch, etc.), or any other device that
a user can wear. A user wearable device 108 may include one or more
sensors that are housed by and/or otherwise integrated with the
device. For example, a user wearable device 108 that is a
smartwatch may include motion sensors (for example,
accelerometers), bio-impedance sensors, electrocardiogram (ECG)
sensors, ballistocardiogram sensors, acoustic sensors (for example,
ultrasound sensors), photo plethysmographic (PPG) sensors that can
use light-based technology to sense a rate of blood flow, and other
sensors. The PPG sensor may be configured to generate a PPG signal
and communicate the PPG signal to at least one programmable
processor of wearable device 108 worn by a user, for example.
Wearable device 108 may also be considered herein to include
sensors that are worn on a user's body but not integrated within
the main wearable portion (for example, an ECG sensor worn on a
user's chest that is not integrated with a smartwatch, but which
nevertheless communicates with the smartwatch).
[0032] FIG. 2 illustrates wearable device 108, including processing
circuitry 202, sensor(s) 204, wearable user interface 206, wearable
device application 208, and memory 210. As noted, sensor(s) 204 may
include multiple sensors integrated with the main wearable portion
of the device and/or sensors located elsewhere on the user's body.
The wearable device application 208, and signals from sensor(s)
204, may be stored in memory 210.
[0033] Wearable user interface 206 is configured to provide an
interface between user wearable device 108 and/or system 100 (FIG.
1) and a user through which the user may provide information to and
receive information from user wearable device 108 and/or system
100. This enables data, cues, results, and/or instructions and any
other communicable items, collectively referred to as
"information," to be communicated between a user and one or more of
the components of system 100 shown in FIG. 1 and/or the components
of wearable device 108 shown in FIG. 2. A user may interact with
wearable user interface 206, for example, to enter data such as
age, height, weight and gender, or to view measured or calculated
metrics such as heart rate, pulse rate variability, stress level,
breathing guidance, and the like. Examples of interface devices
suitable for inclusion in user wearable user interface 206 comprise
a keypad, buttons, switches, a display screen, a touch screen,
speakers, a microphone, an indicator light, an audible alarm, a
tactile feedback device, and/or other interface devices. In short,
any technique for communicating information with system 100 is
contemplated by the present disclosure as wearable user interface
206.
[0034] Wearable device application 208 may run on processing
circuitry 202 and perform such operations as receiving signals from
sensor(s) 204, calculating various health characteristics,
outputting the display of information, providing health guidance to
the user, etc. Wearable device application 208 and/or processing
circuitry 202 are configured to provide information processing
capabilities in wearable device 108 and/or system 100. Processing
circuitry 202 may comprise one or more of a digital processor, an
analog processor, and a digital circuit designed to process
information, an analog circuit designed to process information, a
state machine, and/or other mechanisms for electronically
processing information. Although processing circuitry 202 is shown
in FIG. 2 as a single entity, this is for illustrative purposes
only. In some implementations, processing circuitry 202 may
comprise a plurality of processing units. These processing units
may be physically located within the same device (e.g., user
wearable device 108), or processing circuitry 202 may represent
processing functionality of a plurality of devices operating in
coordination. In some implementations, processing circuitry 202 is
configured to execute one or more computer program modules. In some
implementations, wearable device application 208 may include the
one more computer program modules (e.g., programming instructions
configured to cause user wearable device 108 to function as
described herein). Processing circuitry 202 may be configured to
execute the modules by software; hardware; firmware; some
combination of software, hardware, and/or firmware; and/or other
mechanisms for configuring processing capabilities.
[0035] In some implementations, memory 210 comprises electronic
storage media that electronically stores information. The
electronic storage media of memory 210 may comprise one or both of
system storage that is provided integrally (i.e., substantially
non-removable) with a user wearable device 108 and/or removable
storage that is removably connectable to a user wearable device 108
via, for example, a port (e.g., a USB port, a firewire port, etc.)
or a drive (e.g., a disk drive, etc.). Memory 210 may comprise one
or more of optically readable storage media (e.g., optical disks,
etc.), magnetically readable storage media (e.g., magnetic tape,
magnetic hard drive, etc.), electrical charge-based storage media
(e.g., EPROM, RAM, etc.), solid-state storage media (e.g., flash
drive, etc.), and/or other electronically readable storage media.
Memory 210 may store software algorithms, information determined by
processing circuitry 202 and/or application 208, information
received from a user, and/or other information that enables system
100 to function properly. Memory 210 may be (in whole or in part) a
separate component within a user wearable device 108, or memory 210
may be provided (in whole or in part) integrally with one or more
other components of a user wearable device 108 (e.g., processing
circuitry 202).
[0036] In some implementations, processing circuitry 202, wearable
device application 208, memory 210, and/or other components may
comprise a computer program product comprising a non-transitory,
machine-readable medium storing instructions which, when executed
by at least one programmable processor, cause the at least one
programmable processor to perform various operations (e.g., as
described below). The operations may comprise, for example,
receiving, at the at least one programmable processor, the PPG
signal communicated by the PPG sensor.
[0037] Wearable device 108 may be calibrated and/or otherwise
configured (or reconfigured) during a calibration period. The
calibration may be caused and/or performed by a user, processing
circuitry 202, user wearable device application 208, and/or other
components of system 100. For example, a user may wear device 108
for a 24-hour calibration period upon first use to allow for the
collection of user information from sensor(s) 204. The collection
of characteristics such as pulse rate or respiration rate over a
period of time may facilitate device calibration and provide user
information helpful in the future analysis of signals and the
provision of health guidance to the user. In some implementations,
the calibration may be performed while the user is wearing a single
lead ECG or other sensor(s) for reference purposes.
[0038] Returning to FIG. 1, communication devices 102, 104, and 106
may include any type of mobile or fixed device, for example, a
desktop computer, a notebook computer, a smartphone, a tablet, or
other communication device. Users may, for instance, utilize one or
more communication devices 102, 104, and 106 to interact with one
another, with one or more wearable devices, one or more servers, or
other components of system 100.
[0039] FIG. 3 illustrates some components of an exemplary
communication device 102, 104, and/or 106 including processing
circuitry 302, memory 304, user interface 306, and communication
device application 308. The processing circuitry 302, memory 304,
and user interface 306 function similarly to the processing
circuitry 202, memory 210, and user interface 206, respectively, in
FIG. 2, although the application and user interface of a
communication device will commonly have greater functionality than
that of a wearable device.
[0040] In some implementations, communication device application
308 may be a mobile application (for example, a smartphone
application), or a web application. The communication device
application 308, in some implementations, can communicate with the
user wearable device application 208 via Bluetooth (or any other
method of wired or wireless communication) and/or may transmit
measurements for archival and post-processing to a cloud-based
database (for example, database(s) 118). The communication device
application 308 may aggregate data from the other sensors (for
example, from user monitoring devices 110 and 112), perform
pre-transmission processing locally, and transmit data for further
processing or viewing. In some implementations, processing
circuitry 302, communication device application 308, memory 304,
and/or other components may comprise a computer program product
comprising a non-transitory, machine-readable medium storing
instructions which, when executed by at least one programmable
processor, cause the at least one programmable processor to perform
various operations (e.g., as described below).
[0041] Returning to FIG. 1, in some implementations, user
monitoring devices 110 and 112 may include a blood pressure
monitoring device (for example, a blood pressure cuff), a weight
monitoring device (for example, a scale), a blood glucose
monitoring device, etc. User monitoring devices 110 and 112 may
measure health states of the user different from the health states
measured by user wearable device(s) 108.
[0042] The health monitoring and guidance systems and methods
detailed herein typically utilize signals coming from one or more
sensors that may be in contact with a user's body and that are
sensing information relevant to the user. Sensors can be integrated
with a wearable device (e.g., a wearable device 108), communicating
with a wearable device, or can instead be separate from a wearable
device and communicating with system 100 through other
components.
[0043] As discussed further herein, system 100 can include
components and methods for acquiring particular signals, for
processing such signals (e.g., providing noise reduction), and for
modifying signal acquisition methods. Each of these activities may
be performed by a variety of the components of system 100.
[0044] In some implementations, a user wearable device 108 may
receive an optical signal such as a pulse signal from optical
sensor(s) (e.g., sensors 204 in FIG. 2) utilizing, for example,
green and/or infrared wavelengths of light. Wearable device 108 may
include and/or be operatively coupled with an optical PPG sensor
(e.g., a first sensor 204) configured to generate output signals
conveying information related to the heart rate of a user.
[0045] Wearable device 108 may also include a sensor to capture a
motion signal that may be used to assess noise or interference
resulting from motion of a user wearing device 108 or to assess
other parameters relevant to health analysis and guidance. The
motion signal may be generated by, for example, one or more
accelerometers (e.g., a second sensor 204) included in and/or
otherwise operatively coupled to user wearable device 108. The one
or more accelerometers may be configured to generate one or more
output signals conveying information related to movement and/or
motion of a user, for example.
[0046] In some implementations, processing circuitry 202 (FIG. 2)
and/or a user wearable device application 208 (FIG. 2) may be
configured such that the optical signal, the motion signal, and/or
other signals can be buffered within a memory (for example, memory
210 in FIG. 2) of the user wearable device(s) 108 for a
predetermined time period, and the optical signal and the motion
signal can then be provided to other processors for the processing
of these signals (for example, processing circuitry 302 of
communication device 104 in FIG. 3 or the circuitry of data
analysis device(s) 114). As such, power consumption of the user
wearable device(s) 108 may be conserved or optimized.
Alternatively, in some implementations, the processing circuitry
202 of a user wearable device 108 itself may be used for processing
the optical pulse signal and the motion signal.
[0047] In some implementations, signal collection or acquisition
from an optical sensor (e.g., a sensor 204 shown in FIG. 2) at a
12-50 Hz sampling frequency (for example) may be used. This
sampling frequency may be used when there is a general absence of
user motion combined with low-levels of perfusion and low ambient
light interference and/or at other times. In some implementations,
processing circuitry 202 and/or user wearable device application
(FIG. 2) are configured to automatically determine (e.g., as
described below) the sampling frequency based on various conditions
that can affect the output signals from the sensors including, but
not limited to, the motion of a user wearing the device 108.
[0048] Signal processing challenges caused by user motion may be
overcome by adjusting various parameters relating to signal
acquisition. For example, optical sensor performance can be
adjusted when activity is detected by a motion sensor (e.g., a
three-axis accelerometer). In some implementations, if motion above
a specific threshold is detected, any or all of the following
acquisition parameters of an optical sensor can be adjusted to
overcome the level of noise and to improve the accuracy of health
characteristic determination: (i) sampling frequency, (ii) LED
power, and/or (iii) pulses per sample. Conversely, in some
implementations, if motion below a specific threshold is detected,
then each of these acquisition parameters may be adjusted to
maintain a specific level of performance and measurement precision,
while also conserving power.
[0049] In the general absence of user motion, a sampling frequency
of approximately 20 Hz may be appropriate but, with increased user
movement, for example, sampling can be increased to 100 or 200 Hz
or, if necessary, up to 1000 Hz or more to ensure that signals are
received that are useful for the analysis of user health
characteristics.
[0050] Various health characteristics of a user may be determined
utilizing information in the output signals from sensors discussed
herein (e.g., by processing circuitry 202 and/or application 208 of
a user wearable device 108 shown in FIG. 2, by processing circuitry
302 and/or application 308 of a communication device 102-106 shown
in FIG. 3, by a data analysis device 114, by a server 116, and/or
other components of system 100). As one example, sensors associated
with a user wearable device 108, such as a smartwatch, may be
utilized to determine a user's heart rate, respiration rate, pulse
rate variability (PRV), heart rate variability (HRV), and/or other
health characteristics. Heart rate is typically described as the
number of heartbeats per minute, while HRV and PRV both refer to
variability of time intervals between beats. HRV typically refers
to variability measurements based on electrocardiography and can be
derived from R-R intervals in the standard PQRS waveform. An HRV
determination may utilize an ECG sensor (e.g., a user monitoring
device 110 and/or 112) on a user that may communicate with wearable
device 108. PRV, on the other hand, typically refers to variability
determinations based on sensors placed proximal to peripheral
arteries, such as optical sensor(s) on a user's wrist that provide
a peripheral pulse waveform without the morphology information seen
in an ECG signal.
[0051] User health characteristics may be determined through signal
analysis performed on user wearable device 108 or other components
of system 100 such as communication device 102 or data analysis
device 114, or the analysis may be performed on more than one
component of system 100.
[0052] As shown in FIG. 4A, in some implementations, a received
signal can be an ECG signal 400, and the time 402 at which each
heartbeat has occurred can be determined, for example, from each R
spike 402 in the waveform of signal 400. FIG. 4A illustrates an R-R
interval 409 (e.g., an amount of time between beats) for reference.
Alternatively, the time at which each heartbeat has occurred may be
determined from a PPG signal. FIG. 4A also illustrates a sample PPG
signal 410 and a sample ECG signal 412 for reference.
[0053] As illustrated in FIG. 4B, one method for determining
precise heartbeat times from a PPG signal is to determine the
maximum points in a PPG gradient plot 450. FIG. 4B illustrates an
exemplary method for such detection. In some implementations,
processing circuitry 202, wearable device application 208, memory
210, and/or other components may be configured to detect a
plurality of heartbeats of the user based on the PPG signal. In
some implementations, detecting the plurality of heartbeats
comprises detecting the plurality of heartbeats of the user from a
maximum gradient of the PPG signal. As shown in FIG. 4B, in some
implementations, plot 450 may include an original PPG pulsation
signal (e.g., at 30 Hz) 452 and a corresponding gradient 454 of
signal 452. Determining heartbeat times from the PPG signal may
include determining the locations 456 where gradient 454 is at a
maximum. These locations can correspond to individual beats. A
heart rate of the user may be determined based on at least the
plurality of the determined heartbeats.
[0054] As shown, detecting the plurality of heartbeats may include
performing spline interpolation on local maxima of the maximum
gradient. For example, the spline interpolation 458 may be
performed at individual maximums 456 to accurately determine the
timing of a beat. Improved resolution for such heartbeat
determinations may be obtained through a variety of methods
including, for example, spline interpolation 458 as shown in FIGS.
4B and 4C. As shown in FIG. 4C, a given beat location 456 may be
determined based on gradient 454 maximums using a spline
interpolation 480. FIG. 4C illustrates, at PPG gradient 454 maximum
y(t) 456, spline interpolation 480 for samples y(t-1), y(t), and
y(t+1). System 100 (FIG. 1) may be configured such that y(t-1),
y(t), and y(t+1) are kept in memory. In this example, if a sampling
rate was 25 Hz, time between each sample is 40 ms. Spline
interpolation 480 is performed to determine a location of an R
spike (t.sub.2) with three points of a cubic natural spline.
[0055] In certain implementations, system 100 (e.g., via any of the
processing and/or data analysis components described above related
to FIG. 1-3) may be configured to facilitate a change and/or an
adjustment in sampling rate (e.g., by any of the sensors described
above) associated with the sensor output signals. In some
implementations, the sampling rate may be increased at or near
portions of a signal that correspond to a beat (e.g., at or near
times that correspond to beat locations 456) and/or decreased in
off peak and/or valley areas of plot 450 and/or gradient 454. These
changes and/or adjustments in sampling rate may facilitate power
and/or data storage space savings.
[0056] In some implementations, system 100 (e.g., via any of the
processing and/or data analysis components described above related
to FIG. 1-3) may be configured to identify noisy portions of a
signal and disregard data associated with the noisy portions of the
signal. System 100 may be configured to identify the noisy portions
of a signal based on information from an accelerometer indicating
elevated motion levels, and/or other information. For example,
system 100 may be configured to determine that motion levels have
breached a motion threshold level and exclude signal data for a
period of time when motion levels remain in breach of the motion
threshold. The motion threshold level may be determined at
manufacture of system 100, determined and/or adjusted by a user
(e.g., via a user interface described herein), determined based on
prior monitoring of the user, determined based on historical data
in medical records associated with the user, and/or may be
determined in other ways. In some implementations, responsive to
identifying a noisy portion of a signal, system 100 can be
configured to perform signal enhancement and/or signal
reconstruction operations (e.g., on a PPG sensor signal). After
received signals are analyzed, and precise heartbeat times have
been determined (for example, over a sample time of 10 seconds), a
heart rate in beats per minute can be determined.
[0057] Heart rate variability (HRV) and/or pulse rate variability
(PRV) may be determined based on the plurality of heartbeats (e.g.,
determined as described above). In some implementations, PRV and
HRV may be described as time variances between successive
heartbeats in milliseconds (and/or other increments). Typically, a
number of time deltas between beats are determined and then
statistical analysis is performed to arrive at various indications
of HRV/PRV for the timeframe being examined. This analysis may be
done in the time domain, the frequency domain, and the non-linear
domain (e.g., by any of the processing and/or data analysis
components described above).
[0058] Humans breathe (inhale and exhale) anywhere from 6-20 times
per minute (or in extreme cases even higher). In some
implementations, a user's respiration rate can be determined (e.g.,
by any of the processing and/or data analysis components described
above) from a PPG signal through examination, for example, of the
frequency domain, considering slight heart rate increases observed
every time a person inhales and slight heart rate decreases every
time a person exhales. These small shifts in heart rate, as
calculated, for example, through the time delta between successive
beats, can be analyzed to determine respiration rate.
[0059] In some implementations, system 100 may be configured to
analyze a low frequency component of a PPG signal to determine the
respiration rate of a user such that the respiration rate of the
user may be determined based on the low frequency component of the
PPG signal. In some implementations, there exists a low frequency
component of the PPG signal that is different than a high frequency
component of the PPG signal. The low frequency component of the PPG
signal may be related to respiration (e.g., inhalation and
exhalation) and the high frequency component of the PPG signal may
be related to the plurality of heartbeats. For example, a PPG
signal may include a plurality of high frequency individual
oscillations (that correspond to individual heartbeats) overlaid on
and/or part of a plurality of low frequency oscillations that
correspond to respiration. In some implementations, an increasing
portion of a given low frequency oscillation may correspond to an
inhalation, and a decreasing portion of the given low frequency
oscillation may correspond to an exhalation.
[0060] Returning to FIG. 1, user characteristics such as those
described above can aid the present system in performing various
health assessments. For example, on a basic level, a user's resting
heart rate, in conjunction with some background information, can be
used by system 100 to provide a general assessment of a user's
health. Such assessments may be made through calculations on a
wearable device 108, a communication device 104, or other devices
in system 100.
[0061] Subsequent to such health assessments, system 100 may
provide guidance to a user through various outputs to a user
interface 206 or 306, for example. Such guidance may be in the form
of information, or it may provide feedback in a manner designed to
alter user behavior, as discussed further below. In addition,
system 100 may be configured to provide information to a health
professional or other individual, or to store or analyze
information in particular databases or servers.
[0062] To facilitate health assessments, system 100 can develop a
profile for a user that provides a composite characterization of
the state of the user's autonomic nervous system (ANS) tone,
reflecting the overall influence of sympathetic and parasympathetic
nervous systems on the cardiovascular system.
[0063] The profile for a user can be established as a baseline,
typically prior to the start of an assessment or prolonged
measurement. This can include (but is not limited to) facilitation
of entry and/or selection of information related to a user's age,
gender, height, weight, BMI, and/or other information;
determination (e.g., based on the information in the output signals
from one or more of the sensors described above) minimum heart rate
(if known or previously measured), maximum heart rate (if known or
previously measured), HRV statistics in the time domain (e.g.,
RMSSD--Root Mean Square of the Successive Differences, and pNN50,
and the Mean Absolute Deviation can be evaluated to determine the
normal range for a user), HRV statistics in the non-linear domain
(e.g., Low Frequency Power and High Frequency Power to measure the
parameters that effect HRV and the cardiac cycle that usually occur
at a frequency lower than heart rate (namely respiration rate)), a
determination of whether a user experiences atrial fibrillation,
and/or other information.
[0064] The parameters for the profile can be received from the user
wearable device(s) 108, communication devices 102, 104, and 106,
server(s) 116, user monitoring devices 110 and 112, and/or other
components of the system 100. For example, personal data such as
height, weight, and age of the user can be received from
communication device 102, and the blood pressure levels, weight
levels, blood glucose levels, etc., can be received from user
monitoring devices 110 and 112. Additionally, any historical data
of the user (for example, past medical records) can be received
from database(s) 118, for example. In some implementations, the
server(s) 116 may include database(s) 118 that store user data (for
example, historical data of the user, including past medical
records) and processor(s) 117 that authenticate user wearable
device(s) 108 and communication devices 102, 104, and 106.
[0065] Additional parameters for a profile can be derived from a
combination of the entered profile information and additional
measurement. For example, the Mean Absolute Deviation (MAD) is a
calculation of the amount of deviation from the mean heart rate
over some predetermined period of time (for example, a day, a week,
or a month) indicating an amount of variation in the beat-to-beat
intervals (as measured as the time in milliseconds between pulse
peaks).
[0066] Once real-time user characteristics such as heart rate, HRV,
PRV and respiration rate are determined, system 100 is configured
to utilize such characteristics to assess various aspects of user's
health. For example, by determining relationships between
characteristics, system 100 can determine the level of stress
experienced by a user. Determining whether the user is in a
stressed state may be based on the heart rate, the HRV, the
respiration rate, and/or other characteristics of the user. A
stressed state may be and/or be related to a condition of the user
in which the user is feeling anxious, exasperated, concerned,
pressured, tense, and/or otherwise emotionally strained.
[0067] In some implementations, determining whether the user is in
a stressed state may include determining a particular stress level
of the user, which may also be based on the heart rate, HRV, and
respiration rate (and/or other characteristics). The stress level
may be a value and/or other indicator assigned to an amount of
stress felt by the user, and/or an intensity of the stressed state
of the user.
[0068] A human's autonomic nervous system (ANS) includes two
primary components: the sympathetic and the parasympathetic nervous
system. When a user experiences physical, mental, or emotional
stress, the sympathetic nervous system is stimulated to enable the
user to adapt to the stress. Conversely, when an individual
experiences a relaxation response (or stress recovery), the
parasympathetic nervous system is activated to enable the
individual to recover from the stress.
[0069] Acute stress may be defined as short periods of intense
physiological expression or adaptation to real or perceived threats
to a user. Acute stress can be a highly effective response to a
threat (for example, enabling fight or flight behaviors) and can be
life-preserving. Conversely, chronic stress is a prolonged or
habitual state of stress, even after dissipation of a threat.
Chronic stress can hyper-activate the inflammatory system and HPA
(Hypothalamus Pituitary Axis), causing the sympathetic nervous
system (Fight or Flight) to become overactive and inhibit the
parasympathetic system (Rest and Digest), which can prevent
recovery, an essential part of the healing process.
[0070] When the heart rate of a user is measured and HRV or PRV are
determined, those values can be compared (e.g., by any of the
processing and/or data analysis components described above) to
values of the heart rate, respiration rate, HRV/PRV, atrial
fibrillation, etc., previously included in the user profile. For
example, when the respiration rate/heart rate/HRV/PRV of a user
deviates from typical pathological values by a predetermined
threshold included in the user profile, system 100 may determine
that the user is experiencing a certain level of stress. For
example, if a user is experiencing a heart rate of 60 bpm, while
their minimum Heart Rate (lowest recorded resting heart rate) is 40
bpm, then 60 bpm may indicate a state of stress with a certain
stress level, especially if HRV/PRV is noted to be decreased and
respiration rate is unchanged or increased. Conversely, for a user
with a minimum heart rate of 56 bpm, a current heart rate of 60 bpm
likely means a state of recovery, with a low level of stress. Other
parameters may be used to support such assessments.
[0071] It should be noted that system 100 is configured to account
for pathological irregularities when determining whether a user is
experiencing stress. For example, if a user is prone to atrial
fibrillation, and this condition is indicated in the user's
profile, system 100 is configured to account for (e.g., subtract
out the effect of) this pathological atrial fibrillation when
making a stress determination. Accounting for other similar
pathological irregularities is contemplated.
[0072] In addition to determining that a user is experiencing
stress, a particular level of the stress can be determined. For
example, if a user is currently experiencing a heart rate of 72 bpm
though their minimum heart rate (lowest recorded resting heart
rate) is 40 bpm, then 72 bpm may indicate a low level of stress.
Conversely, if a user is currently experiencing a heart rate of 112
bpm when their minimum heart rate is 40 bpm, then 112 bpm may
indicate a high level of stress.
[0073] In some implementations, determining whether the user is in
a stressed state based on the heart rate, the HRV, the respiration
rate, and/or other characteristics can include comprises
determining a mathematical model for the user, causing the heart
rate, the HRV, the respiration rate, and/or the other
characteristics to be used as inputs into the mathematical model,
and causing the mathematical model to output the determination of
whether the user is in a stressed state based on the inputs. For
example, using the profile, a mathematical model can be built
(e.g., by any of the processing and/or data analysis components
described above), based both on the personal characteristics of the
user and population norms for specific physiological functions.
Continuing with the example, the normal resting heart rate range
(which varies by age and gender) is 55-90 bpm. Maximum heart rate
may be calculated as follows: (a) for men over age 30: 207 minus
70% of age, and (b) for women over age 35: 206 minus 88% of age. In
other words, a mathematical model can be built based on personal
data corresponding to the user and other data corresponding to
population norms, and the user's current stress level can be
determined based on a comparison of the user's current heart rate,
HRV, PRV, and/or respiration rate to the mathematical model. The
mathematical model can be updated when the profile is updated with
additional data.
[0074] In some implementations, stress related determinations can
be further responsive to information in output signals from a
motion sensor (e.g., an accelerometer as described above) so that
determining a stress level may be based on the heart rate, the HRV,
the respiration rate and an activity level determined at least in
part from a motion sensor. In these types of implementations, the
stress determination may also include determination of whether the
activity level of the user breaches a minimum activity threshold
level, for example, to determine whether the user is awake or
asleep. Such minimum activity threshold levels may be determined at
the manufacture of system 100, determined and/or adjusted by a user
(e.g., using a user interface as described above), determined based
on the user profile data for a user, and/or determined in other
ways.
[0075] In some implementations, the mathematical model can include
a weighted combination of heart rate, respiration rate, and heart
rate variability for a user. In some implementations, heart rate
may be weighted more heavily than respiration rate or heart rate
variability.
[0076] In some implementations, the weighted combination of heart
rate, respiration rate, and heart rate variability can comprise a
three feature vector (though other dimensional vectors are
contemplated). System 100 may be configured to analyze the features
of the vector as they vary (e.g., relative to corresponding
thresholds) for a user. Causing the mathematical model to output
the determination of whether the user is in a stressed state can
include analyzing the three (for example) feature vector in a three
(for example) dimensional vector space to determine whether the
vector breaches one or more thresholds that define one or more
stress zones or volumes in the three dimensional vector space. In
this way, the vector may be analyzed in vector space to determine
whether a user is stressed and/or to determine a stress level for
the user.
[0077] These thresholds may be determined based on user profile
information and/or other information. The user profile information
may describe baseline or normal pathological values for the heart
rate, the respiration rate, the HRV, and/or other characteristics
of the user. For example, thresholds for individual features (e.g.,
heart rate, respiration rate, heart rate variability) may be
determined. The thresholds may be determined based on the profile
information as described above (e.g., based on baseline or normal
pathological values (e.g., an overnight heart rate) for individual
features for example), real time or near real time information in
sensor output signals, and/or other information. The thresholds for
individual features may be used in vector space to define one or
more stress zones or volumes. The mathematical model may be
configured such that, responsive to at least a portion of the
(e.g., three) feature vector passing through and/or being bounded
by the one or more stress zones, the mathematical model may
determine that the user is stressed.
[0078] Similarly, the mathematical model may be configured to
determine a particular stress level based on a position of the
vector relative to one or more of the thresholds, a magnitude
and/or length of the vector, and/or other information. For example,
the mathematical model may output a length of the vector that is,
or is indicative of, the stress level. As another example, the
model may output an indication of a relative distance between the
vector (or an end of the vector) and one or more of the thresholds
that is indicative of the stress level.
[0079] The amount of time spent in a sympathetic dominant (stress)
state relative to the time spent in a parasympathetic dominant
state, for a given timeframe (for example, a day or a week), can be
derived from regular measurement of the user's current heart rate,
HRV, PRV, and/or respiration rate (e.g., using the mathematical
model described above)--allowing the user a key insight into their
health and providing an opportunity for intervention if the
sympathetic stress state is dominant for too long. This is evident
when the net time difference between the two states indicates an
on-going net depletion of resources that needs to be replenished
(or recovered) through stress management intervention including,
for example: structured breathing exercises, meditation, higher
quality sleep, and (in most cases other than stress caused by
over-training) increased physical activity.
[0080] Other lifestyle changes can also enhance recovery and reduce
chronic stress. These include, but are not limited to, reduction in
caffeine consumption after mid-afternoon, minimizing use of
electronics and physical exercise just prior to bed, and
improvements in diet by focusing on reductions in sugars and
processed food and increases in plant-based foods.
[0081] Relative to a user's own cardiovascular and ANS performance,
increases in heart rate and respiration rate, accompanied with a
decrease in Heart Rate Variability, are a general indication of
acute stress. If maintained for prolonged periods of time, this
relationship indicates potentially excessive chronic stress.
Real-time measurement of these parameters allows for effective
intervention to reduce chronic stress and potentially reduce
susceptibility to chronic diseases triggered by unmanaged or
uncontrolled prolonged periods of chronic stress.
[0082] System 100 can be configured to provide graphical displays
of information to assist with the assessment of a user's health.
For example, operations performed by the a programmable processor
(e.g., processing circuitry 202, wearable device application 208,
memory 210, and/or other components) may include causing display of
information related to the determination of whether a user is in a
stressed state on a display of wearable device 108 and/or a
different computing device (e.g., communication devices 102-106)
associated with the user. In some implementations, the operations
can include making multiple individual determinations of whether
the user is in a stressed state (e.g., as described above) in an
ongoing manner for a period of time. In some implementations,
causing display of the information related to the determination of
whether the user is in a stressed state can include causing display
of information related to the multiple individual determinations of
whether the user is in a stressed state. In some implementations,
determining whether the user is in a stressed state includes
determining an amount of time the user is in the stressed state
during the period of time.
[0083] An example of a display utilizing a user's health and/or
activity states over time is illustrated in FIG. 5. FIG. 5
illustrates display 500 for a hypothetical period of time for a
user. In this example, measurements were taken and calculations
made (as described herein) throughout the day (e.g., from 00:00:00
to 23:59:59). In some implementations, the operations performed by
the at least one programmable processor (e.g., processing circuitry
202, wearable device application 208, memory 210, and/or other
components) can include determining whether an activity level is
indicative of sleep, exercise, and/or normal daily activity; and
causing the display of periods of stress, sleep, exercise, and/or
normal daily activity. For example, exemplary display 500 in FIG. 5
indicates health and/or activity states including "Activity" 502,
"Recovery" 504, "Exercise Recovery" 506, and "Stress" 508.
Specifically, display 500 illustrates: activity 510 (for example,
from 8 pm to 9 pm), recovery 512 (for example, from midnight to 8
am), stress 514 (for example, from 4 pm to 7 pm), and exercise
recovery 516 at about 3 pm. From midnight to 8 am, display 500 of
FIG. 5 illustrates mostly a recovery state. From 11 am to 3 pm,
display 500 illustrates mixed stress and recovery states throughout
the workday; from 4 pm to 7 pm, display 500 of FIG. 5 illustrates
low-to-mid level stress intensity (for example, because of a
marathon phone conference); and from 8 pm to 9 pm, display 500
illustrates an exercise activity (for example, exercising on an
elliptical trainer at the gym).
[0084] In one implementation, the amplitude of the states shown in
display 500 of FIG. 5 may be calculated as proportional to the
inverse of the current heart rate minus the minimum heart rate (for
a given user), amplified or reduced by the degree of HRV and the
rate of respiration for the user. As the current heart rate nears
the minimum, and HRV and respiration rate are appropriate for
recovery, the amplitude will approach the maximum. The converse
would be true for an exemplary stress amplitude calculation.
[0085] At night, primarily during sleep, heart rate and respiration
rate are lowered and HRV (or similarly PRV) increases relative to
periods of daytime stress. Even during the night, however, movement
or wakefulness can be detected, and recorded for review by the
user. This can provide insight into both the quantity (amount of
time) and quality (amount of recovery) of sleep. Unmanaged chronic
stress can cause reductions in both the quantity and the quality of
sleep. The deeper a user's relaxation during sleep, the higher the
level of recovery will be indicated.
[0086] Accelerometer data can be examined to determine if the user
is active and, if so, can be used to determine an activity
intensity value. If the patient is not active, then system 100 may
determine if the user is in recovery based on factors such as a
lower heart rate, an increased HRV/PRV, and a reduced respiration
rate (relative to the patient's normal ranges for each). If the
user is not active and not in recovery, system 100 can determine
that the user is in a state of stress.
[0087] In one example, a user may be determined to be in recovery
if he has a minimum heart rate of 47, is currently experiencing a
heart rate of 50, while not being active, and demonstrates
increased heart rate variability and a reduced respiration rate. As
described above, in this example, the amplitude of the recovery
indication in a health state graph such as FIG. 5 may be calculated
as proportional to the inverse of the current heart rate minus the
minimum heart rate amplified or reduced by the degree of HRV and
rate of respiration for the person. As the current heart rate nears
the minimum, and HRV and respiration rate are appropriate for
recovery, the amplitude will approach the maximum. The converse
would be true for an exemplary stress amplitude calculation.
[0088] System 100 can be configured to display various health
assessment data gathered and/or calculated, as previously
described. For example, timelines of health assessment data may be
displayed on wearable device 108, or communication device 102,
through their respective wearable device application 208 or
communication device application 308 (see, e.g., FIGS. 6A-6E).
[0089] For example, as shown in FIG. 6A, a view 600 of health
assessment data may be displayed on wearable device 108. Similar to
display 500 shown in FIG. 5, view 600 comprises a visual indication
of recent periods of stress 602 and non-stress 604. FIG. 6B
illustrates a daily summary view 610 for a busy Thursday workday
(for example). View 610 includes an indication of sleep quality
612, a percentage of the day that stress levels were high 614, and
a timeline that displays heath and/or activity state by time of day
616.
[0090] FIG. 6C illustrates an example view 620 of health assessment
data that includes an annotated timeline 622 of health and/or
activity states. Annotated timeline 622 indicates when a user was
in states of stress 624, recovery 626, physical activity 628, and
light physical activity 630. Annotated timeline 622 can be overlaid
with a heart rate indication 632 for the user. Annotations on
annotated timeline 622 can include advice and/or summary windows
634 that provide advice and/or activity summaries to the user, and
time stamp graphics 636 or messages 638 that indicate various
events.
[0091] FIG. 6D illustrates a multi-day summary view 650 for three
successive days (Thursday, Friday, Saturday). View 650 includes a
view 651 similar to view 610 described above, and additional
similar views 652 and 654 for Friday and Saturday. Views 651, 652,
and 654 include indications of sleep quality 660, 662, 664,
percentages of the day that stress levels were high 668, 670, 672,
and annotated timelines 674, 676, 678, that display heath and/or
activity state by time of day, along with summary and/or advice
windows 680 (e.g., similar to those shown in FIG. 6C).
[0092] FIG. 6E illustrates a lifestyle assessment summary view 690.
View 690 includes a user characteristics summary portion 692,
additional information 694, a timeline 696 that indicates when a
user was in states of stress 624, recovery 626, physical activity
628, and light physical activity 630. View 690 also illustrates a
body resources portion 698 indicating whether the user's body
resources increased or decreased over a period of three days.
[0093] In one implementation, the user wearable device application
208 can be programmed to update a stress/recovery/sleep/activity
timeline on a beat by beat basis to ensure that it displays
immediate health assessment information both on demand as requested
by the user, and when actionable information is detected.
Actionable information may include the detection of specific stress
events that would benefit from interventions or the detection and
display of activity or recovery events that can re-enforce positive
behavior performed by a user. The wearable device 108 may also be
programmed to trigger haptic feedback to the user in the form of a
mild vibration along with a concise message suggesting actions the
user may perform depending on the type of detected event.
[0094] System 100 can be configured to provide additional, more
detailed information on a communication device 102 (e.g., a mobile
phone or computer), for example. In one example, illustrated in
FIG. 6B, communication device 102 may display a detailed health
state graph for a particular day. Such a display may indicate
varying user states such as stress, recovery and physical activity
in different colors on a bar graph and may indicate the percentage
of the day spent in each state to allow a user to understand just
how taxing a particular day may have been on his or her body.
[0095] The exemplary display of FIG. 6B illustrates that periods of
recovery, stress or physical activity can be denoted by different
colored columns (e.g., green, red and blue) or shading patterns,
respectively. In addition to color-coded and/or shaded bar charts,
additional assessment information can be provided in the display,
for example, warnings regarding poor sleep quality/duration can be
given, as well as positive feedback relating to exercise.
[0096] FIG. 6C illustrates an additional exemplary display
including more detailed information. Heart rate throughout the day
can be displayed, as well as indications of light or strenuous
physical activity and commentary regarding the health effects of
various states observed during the day. In addition, icons such as
a suitcase or a bed, along with corresponding bars across the time
axis, can be displayed as the result of user-entered journaling of
activities. For example, a suitcase may indicate a commute to or
from work, and a bed may indicate sleep.
[0097] FIG. 6D illustrates an additional exemplary display showing
a user how his or her body has been affected by activities over a
number of days. In addition to bar charts showing the user's states
throughout a few days and nights, additional indications can be
provided regarding percentages of stress versus recovery state,
sleep quality and exercise activity.
[0098] FIG. 6E illustrates an additional exemplary display showing
an overall assessment of a user's body resources over time,
indicating periods of resource depletion and resource recovery--and
showing a cumulative state over time that may be determined by
summation of the additive values of recovery, less the subtractive
values of stress.
[0099] In some implementations, the operations performed by the at
least one programmable processor (e.g., processing circuitry 202,
wearable device application 208, memory 210, and/or other
components) may include generating and causing display of a
recommendation for lifestyle changes determined by the amount of
time the user is in the stressed state and/or other information. In
some implementations, the operations performed by the a processor
can further include, responsive to a determination that the user is
in a stressed state, determining breathing guidance and causing
display of the breathing guidance to the user on the display of a
wearable device to facilitate stress reduction.
[0100] For example, system 100 may be designed to alert a user to
specific events, for example, with haptic feedback through wearable
device 108. In one example, an alert may be given when a period of
high stress is observed. After such an alert, health-related
guidance may be given to the user, such as breathing guidance.
[0101] An effective intervention to reduce stress and potentially
reduce susceptibility to chronic diseases is to optimize breathing
during periods of prolonged stress. An optimal respiration rate for
an average person not currently experiencing respiratory health
issues can be considered less than 10 breaths per minute, although
this level can be personalized given respiration patterns and
health conditions of a particular user.
[0102] Breathing guidance can be displayed to a user via a display
on a user wearable device 108 or a display of communication device
102. The breathing guidance can be presented to the user in
real-time as needed or presented on-demand. For example, when
system 100 determines that the user's stress level is above a
predetermined threshold for a prolonged period of time, the system
can provide breathing guidance to the user. Alternatively, the user
and/or a medical professional can request that breathing guidance
be provided at predetermined intervals of time.
[0103] Components of system 100 (for example, the display on
wearable device 108) can be configured to display graphical
breathing guidance to user. FIG. 7 illustrates a simple exemplary
display 703 that may be shown on wearable device 108. Such a
display can be synchronized to a user's breath, or to a desired
reduced breathing rate. In the example shown in FIG. 7, inner
circular shape 701 may expand and/or contract in diameter relative
to outer circular shape 702 to indicate to the user when to breath
in and/or out for example. This exemplary display is not intended
to be limiting.
[0104] In some implementations, a user's PRV and/or HRV, baseline
pathological (e.g., user profile) information, and/or other
information can be used to determine an optimal breathing rate for
the user in view of the user's current respiration rate. When
activated through a set of programmable triggers or manually
activated by the user, breathing guidance can be determined based
on the current breathing rate of the user (e.g., as determined from
the user's respiration rate derived through frequency domain
analysis of PRV statistics as measured from an optical sensor).
System 100 can then be programmed to select, as breathing guidance,
a breathing rate that is slightly slower (e.g., 1-2 breaths per
minute slower) than the current respiration rate. The user's heart
rate and HRV/PRV are continuously monitored throughout the
breathing guidance and, as the intensity of the current stress
level of the user is reduced, the breathing guidance can further
reduce the desired breaths per minute until either a) breaths per
minute is less than 10, or b) heart rate and/or HRV/PRV are reduced
appropriately.
[0105] Given that the respiration rate, heart rate, HRV and PRV are
being monitored, the effectiveness of the breathing exercise can be
determined. Ideally, there is a positive and quantifiable change in
heart rate, respiration rate, and HRV/PRV during the breathing
exercises. If the exercise is not effective in altering these
parameters, the amount of time inhaling or exhaling can be
increased or modified, along with guidance on the optimal
inhalation and exhalation techniques. This may prevent instances of
over breathing, which may inhibit the desired relaxation effect of
the breathing exercise.
[0106] Using such methods, system 100 can assess both the
effectiveness of breathing exercises, and present feedback to the
user performing the exercise, so he or she can see the immediate
effect of the exercise on their current level of stress and
physiology. In some implementations, system 100 can be configured
to cause display of a goal respiration rate and a current
respiration rate on a continuum. In other implementations, system
100 can be configured to cause display of individual goal/actual
inhalation times and/or goal/actual exhalation times. This provides
a positive feedback mechanism that may encourage the user to
continue the exercise, as they can directly and quantifiably see
the effect on their physiology, and it may therefore help to
develop an effective tool for stress management. When a user
observes (or is alerted) to the occurrence of prolonged periods of
stress, the user can immediately remedy with breathing intervention
methods to reduce stress, potentially preventing deterioration of
health, and enhancing wellbeing.
[0107] Although a few embodiments have been described in detail
above, other modifications are possible. The present disclosure
contemplates that the calculations disclosed in the embodiments
herein may be performed in a number of ways, applying the same
concepts taught herein, and that such calculations are equivalent
to the embodiments disclosed.
[0108] One or more aspects or features of the subject matter
described herein can be realized in digital electronic circuitry,
integrated circuitry, specially designed application specific
integrated circuits (ASICs), field programmable gate arrays (FPGAs)
computer hardware, firmware, software, and/or combinations thereof.
These various aspects or features can include implementation in one
or more computer programs that are executable and/or interpretable
on a programmable system including at least one programmable
processor, which can be special or general purpose, coupled to
receive data and instructions from, and to transmit data and
instructions to, a storage system, at least one input device, and
at least one output device. The programmable system or computing
system may include clients and servers. A client and server are
generally remote from each other and typically interact through a
communication network. The relationship of client and server arises
by virtue of computer programs running on the respective computers
and having a client-server relationship to each other.
[0109] These computer programs, which can also be referred to
programs, software, software applications, applications,
components, modules, or code, include machine instructions for a
programmable processor, and can be implemented in a high-level
procedural language, an object-oriented programming language, a
functional programming language, a logical programming language,
and/or in assembly/machine language. As used herein, the term
"machine-readable medium" (or "computer readable medium") refers to
any computer program product, apparatus and/or device, such as for
example magnetic discs, optical disks, memory, and Programmable
Logic Devices (PLDs), used to provide machine instructions and/or
data to a programmable processor, including a machine-readable
medium that receives machine instructions as a machine-readable
signal. The term "machine-readable signal" (or "computer readable
signal") refers to any signal used to provide machine instructions
and/or data to a programmable processor. The machine-readable
medium can store such machine instructions non-transitorily, such
as for example as would a non-transient solid-state memory or a
magnetic hard drive or any equivalent storage medium. The
machine-readable medium can alternatively or additionally store
such machine instructions in a transient manner, such as for
example as would a processor cache or other random access memory
associated with one or more physical processor cores.
[0110] To provide for interaction with a user, one or more aspects
or features of the subject matter described herein can be
implemented on a computer having a display device, such as for
example a touch screen, a cathode ray tube (CRT), or a liquid
crystal display (LCD) or a light emitting diode (LED) monitor for
displaying information to the user and a keyboard and a pointing
device, such as for example a mouse or a trackball, by which the
user may provide input to the computer. Other kinds of devices can
be used to provide for interaction with a user as well. For
example, feedback provided to the user can be any form of sensory
feedback, such as for example visual feedback, auditory feedback,
or tactile feedback; and input from the user may be received in any
form, including, but not limited to, acoustic, speech, or tactile
input. Other possible input devices include, but are not limited
to, touch screens or other touch-sensitive devices such as single
or multi-point resistive or capacitive trackpads, voice recognition
hardware and software, optical scanners, optical pointers, digital
image capture devices and associated interpretation software, and
the like.
[0111] In the descriptions above and in the claims, phrases such as
"at least one of" or "one or more of" may occur followed by a
conjunctive list of elements or features. The term "and/or" may
also occur in a list of two or more elements or features. Unless
otherwise implicitly or explicitly contradicted by the context in
which it used, such a phrase is intended to mean any of the listed
elements or features individually or any of the recited elements or
features in combination with any of the other recited elements or
features. For example, the phrases "at least one of A and B;" "one
or more of A and B;" and "A and/or B" are each intended to mean "A
alone, B alone, or A and B together." A similar interpretation is
also intended for lists including three or more items. For example,
the phrases "at least one of A, B, and C;" "one or more of A, B,
and C;" and "A, B, and/or C" are each intended to mean "A alone, B
alone, C alone, A and B together, A and C together, B and C
together, or A and B and C together." Use of the term "based on,"
above and in the claims is intended to mean, "based at least in
part on," such that an unrecited feature or element is also
permissible.
[0112] The subject matter described herein can be embodied in
systems, apparatus, methods, computer programs and/or articles
depending on the desired configuration. Any methods or the logic
flows depicted in the accompanying figures and/or described herein
do not necessarily require the particular order shown, or
sequential order, to achieve desirable results. The implementations
set forth in the foregoing description do not represent all
implementations consistent with the subject matter described
herein. Instead, they are merely some examples consistent with
aspects related to the described subject matter. Although a few
variations have been described in detail above, other modifications
or additions are possible. In particular, further features and/or
variations can be provided in addition to those set forth herein.
The implementations described above can be directed to various
combinations and sub combinations of the disclosed features and/or
combinations and sub combinations of further features noted above.
Furthermore, above described advantages are not intended to limit
the application of any issued claims to processes and structures
accomplishing any or all of the advantages.
[0113] Additionally, section headings shall not limit or
characterize the invention(s) set out in any claims that may issue
from this disclosure. Further, the description of a technology in
the "Background" is not to be construed as an admission that
technology is prior art to any invention(s) in this disclosure.
Neither is the "Summary" to be considered as a characterization of
the invention(s) set forth in issued claims. Furthermore, any
reference to this disclosure in general or use of the word
"invention" in the singular is not intended to imply any limitation
on the scope of the claims set forth below. Multiple inventions may
be set forth according to the limitations of the multiple claims
issuing from this disclosure, and such claims accordingly define
the invention(s), and their equivalents, that are protected
thereby.
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