U.S. patent application number 16/813503 was filed with the patent office on 2020-09-03 for virtual reality guided meditation with biofeedback.
The applicant listed for this patent is The StayWell Company, LLC. Invention is credited to Aaron Serling Goldberg, Alex Jeffrey Goldberg.
Application Number | 20200275848 16/813503 |
Document ID | / |
Family ID | 1000004842913 |
Filed Date | 2020-09-03 |
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United States Patent
Application |
20200275848 |
Kind Code |
A1 |
Goldberg; Alex Jeffrey ; et
al. |
September 3, 2020 |
VIRTUAL REALITY GUIDED MEDITATION WITH BIOFEEDBACK
Abstract
A guided meditation system, such as a virtual reality (VR)
guided meditation system, provides biofeedback. Wearable devices
(e.g., fitness trackers) record information about a physiological
state of a user (e.g., heart rate, blood pressure, sleep, and
activity data). Performing guided meditation exercises may help
users improve their physiological state (e.g., by decreasing a
user's heart rate). The VR guided meditation system automatically
retrieves physiological state information from wearable devices or
third party applications before and/or after a user performs guided
meditation exercises. Based on the retrieved information, the VR
guided meditation system provides biofeedback related to effects or
potential correlations between an exercise and the user's
physiological state. In particular, the biofeedback indicates a
certain type of meditation, certain VR environment location, or
certain meditation duration that is likely to improve the user's
physiological state. The VR guided meditation system also can
provide recommended exercises to the user.
Inventors: |
Goldberg; Alex Jeffrey;
(Lake Oswego, OR) ; Goldberg; Aaron Serling;
(Portland, OR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The StayWell Company, LLC |
Yardley |
PA |
US |
|
|
Family ID: |
1000004842913 |
Appl. No.: |
16/813503 |
Filed: |
March 9, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
15162572 |
May 23, 2016 |
10631743 |
|
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16813503 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/486 20130101;
A61B 5/165 20130101; A61B 5/0482 20130101; A61B 5/7264 20130101;
A61B 5/02438 20130101; A61B 5/0205 20130101; A61B 5/6801
20130101 |
International
Class: |
A61B 5/024 20060101
A61B005/024; A61B 5/0205 20060101 A61B005/0205; A61B 5/00 20060101
A61B005/00; A61B 5/16 20060101 A61B005/16 |
Claims
1. A computer-implemented method, comprising: receiving, at a
server from a client device of a user, a request for a guided
meditation exercise; in response to the request from the user,
retrieving information associated with the user; generating
information of a virtual reality environment for the guided
meditation exercise by inputting the retrieved information into a
machine learning model, the machine learning model trained using
information associated with guided meditation exercises of other
users; and providing, by the server, the generated information of
the virtual reality environment for the guided meditation exercise
to the client device for the client device to display the virtual
reality environment for the guided meditation exercise to the user.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 15/162,572, filed May 23, 2016, which is incorporated by
reference in its entirety.
BACKGROUND
1. Field of Art
[0002] This disclosure relates generally to the field of guided
meditation, and specifically to providing guided meditation to a
user in, for example, a virtual reality environment along with
biofeedback.
2. Description of the Related Art
[0003] Meditation can provide numerous physical and mental
benefits. For example, on a physical level, meditation may increase
a person's energy level, lower high blood pressure, improve the
immune system, and reduce tension-based pain. On a mental level,
meditation may, for example, decrease stress and anxiety, increase
happiness, improve emotional stability, and achieve peace of mind.
People who practice meditation regularly are more likely to
experience these benefits. Guided meditation is a form of
meditation in which a person follows voice instructions, either
live or recorded, guiding the person step-by-step through a
meditation exercise.
[0004] Meditating outdoors in nature may facilitate improved
meditation experiences compared to meditating indoors. Natural
environments such as beaches, oceans, forests, waterfalls, and
other pleasant settings can help people relax and focus while
meditating. However, it may be impractical for people who do not
live or work near these natural environments to meditate in natural
environments. In addition, current meditation exercises provide no
way to measure how the meditation is affecting the physiological
condition of the user and what types of meditation might be more
effective for the user.
SUMMARY
[0005] A guided meditation system, such as a virtual reality (VR)
guided meditation system, provides biofeedback. Virtual reality
technology can let users view different relaxing environments
through a virtual reality system. For example, the virtual
environment may be a natural environment located across the world
from the location of a user in real life. Wearable devices such as
fitness trackers record information about a physiological state of
a user, for example, heart rate, blood pressure, sleep, and
activity data. Performing guided meditation exercises may help
users improve their physiological state, for example, by decreasing
a user's heart rate. The VR guided meditation system automatically
retrieves or can receive physiological state information from
wearable devices or third party applications before, during, and
after a user performs guided meditation exercises. Based on the
retrieved information, the VR guided meditation system provides
biofeedback to the user indicating how the user's physiological
state may have changed after the meditation relative to the
physiological state before the meditation (e.g., lower heart rate
or blood pressure). In particular, the biofeedback may indicate a
certain type of meditation, a certain VR environment location, or a
certain meditation duration that is likely to improve the user's
physiological state. Furthermore, the VR guided meditation system
can also provide recommended meditation exercises customized for
the user, e.g., recommending the particular meditation exercise
that resulted in the lowest heart rate, lowest blood pressure, best
sleep patterns, or other affects that suggest that the user is in a
more positive physiological state than before the meditation.
[0006] According to one embodiment, a method begins with receiving
information from a client device of a user requesting a guided
meditation exercise. VR environment information associated with the
guided meditation exercise is provided to the client device for the
client device to display a virtual reality environment perceptible
to the user during the duration of the guided meditation exercise.
Pre-exercise information about a physiological state of the user is
received before the user starts the exercise. Steps of the guided
meditation exercise are provided to the client device.
Post-exercise information about a physiological state of the user
is received after the user starts the exercise. A report is
generated based on statistics of the pre-exercise information and
the post-exercise information. In some embodiments, a machine
learning model is trained to generate recommended guided meditation
exercises based on previous guided meditation exercises performed
by users.
BRIEF DESCRIPTION OF DRAWINGS
[0007] FIG. 1 is a block diagram of a computing environment for
guided meditation with a VR guided meditation system according to
one embodiment.
[0008] FIG. 2 is a block diagram of the VR guided meditation system
within the computing environment of FIG. 1 according to one
embodiment.
[0009] FIG. 3A is a user interface illustrating heart rate
biofeedback according to one embodiment.
[0010] FIG. 3B is a user interface illustrating meditation
performance biofeedback according to one embodiment.
[0011] FIG. 3C is a user interface illustrating biofeedback trends
according to one embodiment.
[0012] FIG. 4 is a data flow diagram illustrating interactions
between data of the VR guided meditation system for training a
model for generating meditation exercise recommendations according
to one embodiment.
[0013] FIG. 5 is a flow chart illustrating a process for providing
guided meditation according to one embodiment.
[0014] The figures depict embodiments of the present invention for
purposes of illustration only. One skilled in the art will readily
recognize from the following discussion that alternative
embodiments of the structures and methods illustrated herein may be
employed without departing from the principles of the invention
described herein.
DETAILED DESCRIPTION
I. System Overview
[0015] FIG. 1 is a block diagram of a computing environment for
guided meditation with a VR guided meditation system 100 according
to one embodiment. The VR guided meditation system 100, client
device 110, and one or more health data sources 120 are each
connected to the network 130. A user interacts with the VR guided
meditation system 100 via a user interface 115 of the client device
110. Some embodiments of the VR guided meditation system 100 may
have additional, fewer, and/or different modules than the ones
described herein, and/or have multiple client devices 110 or
multiple health data sources 120. The functions can be distributed
among the modules in a different manner than described in FIG.
1.
[0016] The client device 110 is an electronic device used by a user
of the VR guided meditation system 100 to perform functions such as
executing software applications, consuming digital content,
browsing websites hosted by web servers on the network 130,
downloading files, and the like. For example, the client device 110
may be a mobile device, a tablet, a notebook, a desktop computer,
or a portable computer. The client device 110 includes interfaces
with a display device on which the user may view the user interface
115, webpages, videos and other content. In addition, the client
device 110 provides a user interface (UI), such as physical and/or
on-screen buttons with which the user may interact with the client
device 110 to perform functions such as viewing, selecting, and
consuming digital content such as digital medical records,
webpages, photos, videos, and other content.
[0017] The health data source 120 is a source of physiological
state information about a user of the VR guided meditation system
100. The health data source 120 may be an electronic device such as
a wearable fitness tracker device, for example, APPLE WATCH.RTM.,
FITBIT FLEX.TM., or JAWBONE UP4.TM.. The health data source 120 may
also be a third party application, e.g., APPLE.RTM. HEALTHKIT,
GARMIN.RTM., GOOGLE.RTM. FIT, UNDER ARMOUR.RTM. MAPMYFITNESS, and
STRAVA.RTM.. The health data sources 120 can be devices worn on the
body or on the clothing of the user, such as a device worn on the
wrist, around the neck, around the ankle, on a shirt or a shoe,
sensors embedded within a piece of clothing worn by the user, a
band around the user's chest, arm, or leg, a headband or head
device worn on the user's head, among other options. The
physiological state information includes, e.g., the user's heart
rate, the user's blood pressure, the user's activity (e.g., number
of steps walked, number of miles ran, number of calories burned,
etc.), the user's breathing patterns, the user's sleep activity,
the user's brain waves, the user's pain response, the user's skin
conductance, the user's eye movements, the user's muscle action
potentials, the user's temperature, the user's skin electrical
activity, or the user's blood flow, among other types of health
data.
[0018] The network 130 enables communications among network
entities such as the client device 110, health data source 120, and
VR guided meditation system 100. In one embodiment, the network 130
comprises the Internet and uses standard communications
technologies and/or protocols, e.g., BLUETOOTH.RTM., WiFi,
ZIGBEE.RTM., clouding computing, cellular connectivity, other air
to air, wire to air networks, and mesh network protocols to client
devices, gateways, and access points. In another embodiment, the
network entities can use custom and/or dedicated data
communications technologies.
[0019] In one embodiment, the VR guided meditation system 100
receives a request for a VR guided meditation exercise from a user
via the client device 110. The request indicates a type of
meditation, time duration for meditation, and location for
meditation. Based on the request, the VR guided meditation system
100 provides a VR guided meditation exercise to the user via the
client device 110. Before the user starts the VR guided meditation
exercise, the VR guided meditation system 100 receives
physiological state information about the user from the health data
source 120, i.e., pre-exercise information. After the user starts
the VR guided meditation exercise, the VR guided meditation system
100 again receives physiological state information about the user
from the health data source 120, i.e., post-exercise information.
In some embodiments, the post-exercise information includes
information collected about the user during the meditation exercise
or immediately after the meditation exercise, though it can also
include information collected a while after the meditation
exercise, such as over the next hour or over the rest of the day,
or data collected until in the next meditation exercise occurs. The
VR guided meditation system 100 provides biofeedback to the user
based on the pre-exercise information and the post-exercise
information. For example, the biofeedback indicates that the user's
heart rate is 80 beats per minute (bpm) before starting the VR
guided meditation exercise and that the heart rate lowered to 62
bpm after completing the VR guided meditation exercise. The
biofeedback is displayed on the user interface 115 viewed by the
user on the client device 110.
[0020] FIG. 2 is a block diagram of the VR guided meditation system
100 within the computing environment of FIG. 1 according to one
embodiment. The VR guided meditation system 100 in FIG. 2 includes
a user interface module 200, user account module 205, VR engine
210, guided meditation module 215, health data source manager 220,
biofeedback module 225, recommendation model 230, machine learning
module 235, user account store 240, and VR guided meditation store
245. In other embodiments, the VR guided meditation system 100 may
include additional, fewer, and/or different modules for various
applications. Conventional components such as network interfaces,
security mechanisms, load balancers, failover servers, management
and network operations consoles, and the like are not shown so as
to not obscure the details of the system 100. Also, it is noted
that the modules may be embodied as hardware, software (which may
include firmware), or any combination thereof. For software, it may
include program code or code segments. Software is comprised of one
or more instructions storable in a computer readable non-transitory
storage medium, e.g., a memory or disk, and executable by a
processor.
[0021] The user interface module 200 generates user interfaces of
the VR guided meditation system 100, e.g., user interface 115 shown
in FIG. 1. Additional example user interfaces are further described
with reference to FIGS. 3A-C. In an embodiment, the user interface
module 200 serves web pages, as well as other web-related content,
such as Flash, XML, and so forth. The user interface module 200 can
provide the functionality of receiving and routing messages and/or
information, e.g., between the VR guided meditation system 100,
client device 110, as well as other external systems. These
messages can be instant messages, queued messages (e.g., email),
text and SMS (short message service) messages, or any other
suitable messaging technique. The user interface module 200 allows
the user to view and/or interact with user interfaces (e.g., user
interface 115) of the VR guided meditation system 100 by
communicating information between the VR guided meditation system
100 and the client device 110.
[0022] The user account module 205 stores user account data
associated with users of the VR guided meditation system 100. In an
embodiment, the user account data of a user includes information
including a name of the user, contact information (e.g., email and
phone number) of the user, an employer of the user, information
about VR guided meditation exercises that the user has previously
started and/or completed, biofeedback associated with the user,
recommended VR guided meditation exercises, physiological state
information associated with the user, and the like. The VR guided
meditation system 100 receives the information from a user via the
client device 110, the health data source 120, and/or an external
source such as an online database accessible by the VR guided
meditation system 100 via the network 130.
[0023] The VR engine 210 generates a VR environment associated with
a VR guided meditation exercise generated by the guided meditation
module 215. In an embodiment, the VR engine 210 extracts VR
environment data from the VR guided meditation store 245. The VR
environment data may have been previously input, e.g., via a client
device 110, to the VR guided meditation store 245 by an expert,
e.g., a designer of VR environments. Based on the data, the VR
engine 210 generates the VR environment. The VR environment can be
a live 360 degree view of an environment. For example, the VR
environment is generated based on video captured from a camera with
up to a 360 degree view. In an embodiment, the VR environment
includes one or more visual and/or audio signals corresponding to a
location of the VR environment. For instance, a location of the VR
environment is named "garden falls." Accordingly, the one or more
visual and/or audio signals corresponding to the "garden falls"
location include visual and/or audio signals of waterfalls and
garden plants. In particular, a visual signal is a video imagery of
a waterfall surrounded by trees and plants with flowers. Further,
an audio signal is a sound of water flowing or splashing in the
waterfall. Other types of VR environment locations include, e.g., a
beach, an island, or a forest, etc., and may be associated with
different names such as "paradise beach," "tropical island," or
"peaceful forest," etc. The VR engine 210 provides the VR
environment to the client device 110, via the user interface module
200, for presentation to the user. In particular, the visual
signals (e.g., videos and photos) are presented in a graphical
display of the client device 110, e.g., an electronic display of a
smartphone. Additionally, the audio signals are presented via audio
speakers of the client device 110 and/or another audio playing
device (e.g., headphones or external speakers) communicatively
coupled to the client device 110.
[0024] The guided meditation module 215 generates a VR guided
meditation exercise associated with a VR environment generated by
the VR engine 210. In an embodiment, the VR engine 210 extracts
meditation exercise data from the VR guided meditation store 245.
The meditation exercise data may have been previously input, e.g.,
via a client device, to the VR guided meditation store 245 by an
expert, e.g., a meditation instructor or researcher. Based on the
meditation exercise data, the VR engine 210 generates the VR guided
meditation exercise. In an embodiment, the VR guided meditation
exercise includes meditation instructions corresponding to a type
of meditation. For instance, a type of meditation is named
"breathing." Accordingly, the meditation instructions relate to
breathing of a user. For example, the instructions include "keep
your breath natural" and "notice where you feel your breath in your
body."
[0025] In some an embodiments, the VR guided meditation exercise
has a time duration, which may be selected by the user or
pre-determined. For a VR guided meditation exercise with a short
time duration, e.g., 1 minute, the guided meditation module 215 may
reduce the number of meditation instructions such that the VR
guided meditation exercise can be completed within the shorter time
duration. For a VR guided meditation exercise with a long time
duration, e.g., 30 minutes, the guided meditation module 215 may
increase the number or duration of pauses in between meditation
instructions such that the VR guided meditation exercise can be
completed within the longer time duration. The guided meditation
module 215 provides the meditation instructions to the client
device 110, via the user interface module 200, for presentation to
the user. In particular, the meditation instructions represented by
visual signals (e.g., graphical text of the meditation
instructions) are presented in a graphical display of the client
device 110, e.g., an electronic display of a smartphone.
Additionally, the meditation instructions represented by audio
signals (e.g., an audio narration of the meditation instructions)
are presented via audio speakers of the client device 110 and/or
another audio playing device (e.g., headphones or external
speakers) communicatively coupled to the client device 110.
[0026] The health data source manager 220 facilitates communication
between the VR guided meditation system 100 and the health data
source 120 via the network 130. The health data source manager 220
receives, from the user via the client device 110, a request to
associate one or more health data sources 120 to an account of the
user. Based on the user's account data in the user account store
240, the health data source manager 220 determines whether the
user's account is already associated with the health data sources
120. If the user's account is not associated with at least one of
the health data sources 120, then the health data source manager
220 informs the user to provide login credentials of the health
data sources 120 (that are not already associated with the user's
account). For example, the health data source manager 220 generates
a user interface for display on the client device 110. The user
interface may include text boxes for the user to input the login
credentials. The health data source manager 220 receives the input
login credentials and authenticates the login credentials, e.g.,
using an application programming interface (API).
[0027] In one embodiment, the heath data sources 120 include a
FITBIT.RTM. fitness tracker device and an APPLE.RTM. HEALTHKIT
application. The health data source manager 220 receives login
credentials for each of the health data sources 120. The health
data source manager 220 provides, via an API, login credentials
corresponding to the fitness tracker device to a FITBIT.RTM.
(third-party) application. If the FITBIT.RTM. application
authenticates the login credentials, then the health data source
manager 220 is authorized to retrieve information from an account
of the FITBIT.RTM. application associated with the user. Similarly,
the health data source manager 220 provides, via an API, login
credentials corresponding to the HEALTHKIT application to an
APPLE.RTM. (third-party) application. If the APPLE.RTM. application
authenticates the login credentials, then the health data source
manager 220 is authorized to retrieve information from an account
of the APPLE.RTM. application associated with the user. The health
data source manager 220 stores input login credentials in the user
account store 240 so that the user does not need to provide the
login credentials multiple times. Generally, once the VR guided
meditation system 100 receives authorization to access information
from a health data source 120, the health data source manager 220
can retrieve information about physiological states of users from
the health data source 120. For example, the health data source
manager 220 retrieves pre-exercise information, post-exercise
information, and information while a user is performing a VR guided
meditation exercises, i.e., in progress. In some embodiments, the
health data source manager 220 retrieves information about a user
periodically throughout the day, or at predetermined times (e.g.,
morning, afternoon, and night). In some embodiments, the health
data sources 120 are push systems that provide information to the
VR guided meditation system 100, e.g., without requiring the health
data source manager 220 to request or retrieve the information. For
example, a health data source 120 pushes a user's current heart
rate information to the health data source manager 220 once every
hour.
[0028] The biofeedback module 225 generates biofeedback based on
physiological state information about a user of the VR guided
meditation system 100. The biofeedback is also based on information
about VR guided meditation exercises performed by the user. In one
example (shown in user interface 300 in FIG. 3A), the biofeedback
indicates a heart rate of the user before starting a VR guided
meditation exercise and a heart rate of the user after completing
the VR guided meditation exercise. The biofeedback may be
represented by statistics or visual elements such as different
types of graphs. For instance, the biofeedback includes a graph
(e.g., graph 304 shown in FIG. 3A) illustrating the heart rate of a
user while the user is meditating. The biofeedback may suggest that
performing VR guided meditation exercises helps improve a
physiological state of the user. For instance, the heart rate of
the user before starting the VR guided meditation exercise is 80
bpm, and the heart rate of the user after completing the VR guided
meditation exercise is 62 bpm. Thus, the biofeedback suggests that
performing the VR guided meditation exercise helped lower the
user's heart rate, which is desirable, e.g., because a lower heart
rate indicates that the user is more likely to be less stressed,
and thus healthier.
[0029] In some embodiments, the biofeedback module 225 generates
biofeedback based on demographic information and/or health metrics
of the user. The demographic information describes, e.g., the age,
gender, or geographical location of the user. The health metrics
describe, e.g., the weight, body mass index, blood pressure, or
chronic disease condition of the user. Health metrics may also be
referred to as body metrics, biometrics, or biological indicators.
In one example (shown in the user interface 310 in FIG. 3B), the
biofeedback indicates an average heart rate of the user compared to
an average heart rate of a population of users within the same age
range of the user. In another example, the biofeedback describes a
goal for the user based on the user's age. The goal may indicate a
target amount of hours that the user should sleep on average each
day based on clinical recommendations, e.g., teenagers should sleep
9 hours on average each day.
[0030] In some embodiments, the biofeedback module 225 generates a
report of a user's biofeedback. The report can provide various data
about the user and one or more changes in the user's health metrics
before and after a guided meditation exercise, or biofeedback data
about the user received before, during, and after the guided
meditation exercise. The report can include information about the
user and a comparison of the user to a population of other users
who have also performed guided meditation exercises. As one
example, the report indicates that the user's breathing pace, heart
rate, and blood pressure each decreased about 2 minutes into a
guided meditation exercise. The report might indicate that the
user's weight has decreased within two months of starting guided
meditation exercises. The report might include information about
the user's sleep patterns indicating that the user is sleeping more
peacefully without waking up throughout the night by performing a
particular type guided meditation exercise over another type of
guided meditation exercise (e.g., "breathing" type meditation
appears more correlated with uninterrupted sleep than "body scan"
type meditation). The sleep patterns may also show a stronger
improvement in the user's sleep patterns relative to those of a
population of users performing the same type of meditation
exercise.
[0031] The user may also share the report with another user, for
example, an employer, doctor, or therapist of the user. In an
example use case, a user's doctor may prescribe a VR guided
meditation exercise as part of a treatment for the user, which may
include other types of treatment such as medications or therapies.
Based on the report, the user can determine whether the prescribed
VR guided meditation exercise appears to have helped improve the
user's physiological state or health metrics. The report provided
to the doctor may include the user's own data and/or the user's
data compared to data of a population of other users who have
performed guided meditation exercises in an aggregate report.
Another example use case is where a group of users are employees of
an employer and participating in a workplace wellness program. The
biofeedback module 225 aggregates biofeedback of the group to
generate the report. For instance, the biofeedback module 225
reports the average meditation performance of the employees to the
employer. In yet another example use case, users may share their
biofeedback reports to a health insurance companies or other health
care providers.
[0032] The biofeedback can indicate trends or changes over time of
a physiological state of the user. For example, the average daily
heart rate of a user decreases by 10 bpm over the duration of a
month. The biofeedback may indicate that the trends or changes
appear to be correlated to VR guided meditation exercises, e.g., as
the user performs more "breathing" type VR guided meditation
exercises, the user's heart rate decreases. As another example, the
biofeedback indicates that a user has an average of seven hours of
sleep each night during weeks that the user performed at least five
VR guided meditation exercises, and an average of five hours of
sleep each night during weeks that the user performed less than
five VR guided meditation exercises. Thus, the biofeedback
indicates that the user's average number of hours of sleep is
correlated to the number of VR guided meditation exercises
performed by the user during the week. In addition to
individual-based biofeedback, the trends or changes (as well as the
generated report previously described) can be based on aggregate
information from a population of users or a subpopulation of users.
The users may be categorized into subpopulations based on
demographic data or health metrics of the users. For example, the
biofeedback indicates the average change in heart rate during a
month for users who are categorized as overweight, average weight,
or underweight.
[0033] The recommendation model 230 generates meditation exercise
recommendations based on information about users of the VR guided
meditation system 100. In some embodiments, the recommendation
model 230 is a machine learning model. In one use case of the VR
guided meditation system 100, a user manually selects a type of
meditation exercise (e.g., "body scan," "breathing," or "anxiety"),
a VR environment location (e.g., "garden falls," "coastal pond," or
"paradise beach") to view while performing the meditation exercise,
and/or a duration of meditation (e.g., 2 minutes, 5 minutes, or 10
minutes). In another use case, the user selects a VR guided
meditation exercise automatically suggested by the recommendation
model 230, which saves the user's time and provides a more engaging
user experience because the user does not need to manually select
each option for the meditation exercise. Additionally, since the
recommendation model 230 is trained based on information specific
to a user, the meditation exercise recommendations are customized
for the user. Therefore, the meditation exercise recommendations
are more likely to help the user improve the user's physiological
state. For example, based on previous VR guided meditation
exercises performed by the user, the user's biofeedback indicates
that the user experiences the greatest decrease in blood pressure
when the VR environment of the exercises is a "paradise beach"
location. The machine learning module 235 uses the user's
biofeedback to update the user's recommendation model 230. Thus,
the meditation exercise recommendations suggest that the user
perform VR guided meditation exercises while viewing the "paradise
beach" location more frequently.
[0034] The machine learning module 235 uses machine learning
techniques to generate the recommendation model 230. In particular,
the machine learning module 235 may generate a model based on
optimization of algorithms that analyze information from the user
account store 240 (e.g., demographic information about users,
information about VR guided meditation exercises performed by
users, or biofeedback associated with users). For example, the
machine learning module 235 generates a classifier that takes as
input a set of VR guided meditation exercises performed by a user
and the corresponding biofeedback information. The VR guided
meditation exercises are each associated with a different type of
VR environment location and the biofeedback information describes
the pre-exercise heart rate and post-exercise heart rate of the
user associated with each of the VR guided meditation exercises.
The classifier outputs which of the VR environment locations
corresponds to the greatest decrease in the user's heart rate from
pre-exercise to post-exercise. The recommendation model 230 can use
the output of the classifier--in addition to the output of other
classifiers generated by the machine learning module 235--to
generate meditation exercise recommendations for a certain user. In
some embodiments, the machine learning module 235 uses other
machine learning techniques for generating meditation exercise
recommendations, for example, tree-based models, neural networks,
information retrieval, or any combination thereof.
[0035] In some embodiments, the VR guided meditation system 100
uses multiple recommendation models 230 to generate meditation
exercise recommendations for users. For example, the machine
learning module 235 may divide users into different subsets of
users based on the users' demographic information, e.g., age,
gender, geographic location, ethnicity, and/or health metrics,
e.g., weight, body mass index, blood pressure, chronic diseases,
health condition, and the like. Additionally, the users may be
divided based on other types of information such as employers of
the users, users who are connected via an online system such as a
third-party social networking system. The machine learning module
235 generates one recommendation model 230 for each subset of
users, e.g., because different subsets of users may have different
types of optimal meditation exercises. For instance, to achieve a
10 bpm decrease (pre-exercise to post-exercise) in heart rate,
users in the 50-60 year old range need to meditate on average for
10 minutes, while users in the 20-30 year old range need to
meditate on average for 15 minutes. Thus, a recommendation model
230 customized for a subset of users is more likely to provide more
effective (i.e., more likely to improve the user's physiological
state) meditation exercise recommendations compared to a general
recommendation model 230 for an entire set of users.
[0036] The machine learning module 235 uses training data sets
including features for training the recommendation models 230. The
machine learning module 235 generates training data sets based on
information retrieved from the user account store 240. In one
embodiment, the training data sets are tuples including features,
i.e., information describing demographic information about users,
information about VR guided meditation exercises performed by
users, and/or biofeedback associated with users. In some
embodiments, the machine learning module 235 performs online
training by retrieving training data sets from a global database of
training data accessible over the network 130, e.g., including
aggregated information based on a population of users of VR guided
meditation systems 100. Further, the machine learning module 235
may upload training data sets to the global database. The training
data sets may be organized based on demographic information, e.g.,
training data sets are categorized based on VR guided meditation
exercises performed by teenage users versus VR guided meditation
exercises performed by elderly users. Training data sets are
further described with reference to FIG. 4.
[0037] In some embodiments, the machine learning module 235
periodically retrains recommendation models 230. For example, as a
user performs more VR guided meditation exercises over time using
the VR guided meditation system 100, the machine learning module
235 generates new training data sets based on the VR guided
meditation exercises. The machine learning module 235 may retrain,
using the new training data sets, a recommendation model 230
associated with a given user after each VR guided meditation
exercise performed by the user. The machine learning module 235 may
retrain recommendation models 230 at a rate based on other factors
such as, for example, how frequently a user performs VR guided
meditation exercises, or the quality of biofeedback associated with
a user. For example, the machine learning module 235 retrains
recommendation models 230 more frequently for a user if the user
performs more meditation exercises compared to the average number
of meditation exercises performed (e.g., over a certain period of
time) by a population of users. In another example, if the user's
heart rate is not decreasing after the user has performed at least
a threshold number of meditation exercises recommended by the
recommendation model 230, then the machine learning module 235
retrains a recommendation model 230 associated with the user, e.g.,
using new training data sets from a global database.
II. User Interfaces
[0038] FIG. 3A is a user interface 300 illustrating heart rate
biofeedback according to one embodiment. The user interface 300,
e.g., generated by the biofeedback module 225, includes a selection
302 to display heart rate type biofeedback of a user of the VR
guided meditation system 100. The user interface 300 also includes
a graph 304 and statistics 306 describing a user's heart rate while
meditating. In particular, the x-axis of the graph 304 indicates
time and the y-axis of the graph 304 indicates the user's heart
rate in bpm. In the embodiment shown in FIG. 3A, the graph 304
indicates that user's heart rate gradually decreased over time
during a VR guided meditation exercise. Further, the user interface
300 indicates that the graph 304 is associated with a "body scan"
type VR guided meditation exercise performed by the user on a given
day. In other embodiments, the user interface 300 includes graphs
of biofeedback associated with VR guided meditation exercises
performed by the user over the duration of a month, a year, or any
other duration of time. The statistics 306 indicate that the user's
heart rate before starting the VR guided meditation exercise is 80
bpm and that the user's heart rate after starting the VR guided
meditation exercise is 62 bpm, e.g., corresponding to the
information shown in the graph 304.
[0039] FIG. 3B is a user interface illustrating meditation
performance biofeedback according to one embodiment. The user
interface 320, e.g., generated by the biofeedback module 225,
includes a selection 312 to display biofeedback performance of a
user of the VR guided meditation system 100. The user interface 300
also includes a selection 314 of an age range of users, statistics
316 describing a user's heart rate while meditating, graph 318, and
statistics 320 describing how much time the user meditates. In
particular, the selection 314 indicates an age range of 60-65 years
old. In other embodiments, the age range may be 0-20 years old,
18-25 years old, 65+ years old, or any other suitable age range.
The statistics 316 indicate that the user's heart rate while
meditating (i.e., performing VR guided meditation exercises) is 64
bpm on average. The statistics 316 also indicate that the average
heart rate for other users in the same (e.g., 60-65 year old) age
group while meditating is 72 bpm. Thus, the statistics 316 indicate
that the user's biofeedback performance is better than average for
the selected age group, e.g., because a lower heart rate is more
desirable. The statistics may also be presented as a percentage,
indicating the percentage change in the heart rate of the user in
comparison to other users.
[0040] The graph 318 shows that number of minutes per week that the
user meditates on average compared to other users in the selected
age group. In particular, the graph 318 indicates that the user
performs VR guided meditation exercises for an average of 40
minutes per week and that other users meditate for an average of 60
minutes per week. The statistics 320 indicate the same information
as the graph 318. The graph 318 shown in FIG. 3B is a bar graph,
though it should be noted that in other embodiments, user
interfaces of the VR guided meditation system 100 may include other
types of graphs such as line graphs, pie graphs, histograms,
scatterplots, and the like, as well as other forms of visual
representation of statistics. Additionally, the statistics 316 and
318 indicate average values, though it should be noted that in
other embodiments, user interfaces of the VR guided meditation
system 100 may include other types of statistics such as standard
deviations, confidence intervals, and the like.
[0041] FIG. 3C is a user interface illustrating biofeedback trends
according to one embodiment. The user interface 330, e.g.,
generated by the biofeedback module 225, includes a selection 332
to display biofeedback trends of a user of the VR guided meditation
system 100. The user interface 330 includes statistics describing
the user's biofeedback trends associated with different parameters.
In particular, statistic 334 indicates that the user's average
heart rate while meditating in the morning is 74 bpm. Since 74 bpm
is lower than the user's heart rate while meditating during other
times of the day, e.g., afternoon or evening, the statistic 334
also indicates that morning is the best time for the user to
meditate. Statistic 336 indicates that the user's average heart
rate while performing "body scan" type VR guided meditation
exercises is 60 bpm. Since 60 bpm is lower than the user's heart
rate while performing other types of VR guided meditation
exercises, e.g., "breathing," "anxiety," or "focus," the statistic
336 also indicates that "body scan" is the best type of meditation
exercise for the user. Statistic 338 indicates that the user's
average heart rate while performing VR guided meditation exercises
associated with a "peaceful forest" type VR environment location is
62 bpm. Since 62 bpm is lower than the user's heart rate while
performing VR guided meditation exercises associated with other
types VR environment locations, e.g., "garden falls," "coastal
pond," or "paradise beach," the statistic 338 also indicates that
"peaceful forest" is the best type of VR environment location for
the user.
[0042] The statistic 340 indicates the user's sleep activity
biofeedback associated with VR guided meditation exercises
performed by the user. In particular, the statistic 340 indicates
that the user's average duration of sleep on days that the user
meditated is 7.2 hours, and that the user's average duration of
sleep on days that the user did not meditate is 6 hours. Thus, the
statistic 340 suggests that the user is able to sleep for a longer
duration of time on days that the user meditated, which is
desirable, e.g., because 7.2 hours is closer to the user's target
duration of sleep relative to 6 hours, based on clinical guidelines
(e.g., 7-9 hours of sleep per day for adults). In other
embodiments, statistics indicate a level of activity of the user on
days that the user meditated compared to a level of activity of the
user on days that the user did not meditate. For example, the
statistics indicate that the user walked an average of 8000 steps
on days that the user performed at least one VR guided meditation
exercise and that the user walked an average of 5000 steps on days
that the user did not meditate.
III. Machine Learning Model
[0043] FIG. 4 is a data flow diagram 400 illustrating interactions
between data of the VR guided meditation system 100 for training a
model for generating meditation exercise recommendations according
to one embodiment. In particular, the machine learning module 235
trains the recommendation model 230 using features described by
tuples 420, 430, 440, 450, and 460 of a training data set 410. The
features are based on information describing users and VR guided
meditation exercises performed by users. In one example, a user is
a 25 year old female in Portland, Oreg. The user performs a "body
scan" type VR guided meditation exercise associated with a
"paradise beach" VR environment location for 10 minutes. In
addition, the user's post-exercise heart rate is 10 bpm lower than
the user's pre-exercise heart rate. Accordingly, tuple 420
indicates the meditation type, "body scan." Tuple 430 indicates the
meditation location, "paradise beach." Tuple 440 indicates the
meditation duration, "10 minutes." Tuple 450 indicates the
biofeedback, "heart rate decrease by 10 bpm." Tuple 460 indicates
the user data, "25 year old female in Portland, Oreg."
[0044] In the embodiment shown in FIG. 4, the recommendation model
230 takes as input a meditation type, meditation location,
meditation duration, biofeedback, and user data. Based on the
input, the recommendation model 230 generates a meditation exercise
recommendation. Meditation exercise recommendations may recommend
that a user perform a certain type of VR guided meditation
exercise, perform meditation exercises while viewing a certain VR
environment location, perform meditation exercises for a certain
time duration (or range of time durations), or any combination
thereof. The meditation exercise recommendations may depend on the
user data. For example, a meditation exercise recommendation for a
30 year old male is different than a meditation exercise
recommendation for a 50 year old female. The training data set 410
includes five tuples, though it should be noted that in other
embodiments, the machine learning module 235 uses training data
sets including additional, fewer, or different features (e.g.,
represented by tuples) to train recommendation models 230.
IV. Process Flow
[0045] FIG. 5 is a flow chart illustrating a process for providing
guided meditation according to one embodiment. In some embodiments,
the process 500 is used--for example, by modules of the VR guided
meditation system 100--within the computing environment of FIG. 1.
The process 500 may include different or additional steps than
those described in conjunction with FIG. 5 in some embodiments, or
perform steps in different orders than the order described in
conjunction with FIG. 5.
[0046] The VR guided meditation system 100 receives 510, via the
user interface module 200, user information from a client device of
the user. The information includes a request for a VR guided
meditation exercise and may also include input indicating a type of
meditation, a VR environment location, and a duration of
meditation. The VR engine 210 provides 520 VR environment
information associated with the VR guided meditation exercise to
the client device to display a VR environment to the user during
the duration of the VR guided meditation exercise. The health data
source manager 220 receives 530 pre-exercise information about a
physiological state of the user before the user starts the
exercise. The guided meditation module 215 provides 540 one or more
steps of the VR guided meditation exercise to the client device.
The health data source manager 220 receives 550 post-exercise
information about a physiological state of the user after the user
starts the exercise. The biofeedback module 225 generates 560 a
report (e.g., shown in user interfaces 300, 310, and 330 in FIGS.
3A, 3B, and 3C, respectively) based on statistics using the
pre-exercise information and the post-exercise information. The
recommendation model 230 generates 570 a recommended VR guided
meditation exercise based on the report and/or information about
the user. The user may select to perform the recommended VR guided
meditation exercise.
[0047] In some embodiments, the VR guided meditation system 100
provides VR guided meditation exercises and biofeedback to a user
without providing a VR environment. For example, the VR guided
meditation system 100 provides one or more steps of a VR guided
meditation exercise as audio instructions that the user listens to
without a virtual reality component, with a visual picture but not
presented in a virtual reality environment, with a meditation
guided by a live instructor, among other options. In these use
cases, the user can perform the VR guided meditation exercise with
the user's eyes closed or while looking at an environment other
than a VR environment, for example, a real world environment. Thus,
the user can receive biofeedback with this guided meditation system
in any type of guided meditation environment. Further, the guided
meditation system can similarly collect and analyze physiological
state data and health metrics, compare the user's meditation
performance to those of other users of a population, provide
recommendations based on machine learning models associated with
meditation exercises, provide reports to the user and/or other
parties, etc.
V. Alternative Embodiments
[0048] The foregoing description of the embodiments of the
invention has been presented for the purpose of illustration; it is
not intended to be exhaustive or to limit the invention to the
precise forms disclosed. Persons skilled in the relevant art can
appreciate that many modifications and variations are possible in
light of the above disclosure.
[0049] Some portions of this description describe the embodiments
of the invention in terms of algorithms and symbolic
representations of operations on information. These algorithmic
descriptions and representations are commonly used by those skilled
in the data processing arts to convey the substance of their work
effectively to others skilled in the art. These operations, while
described functionally, computationally, or logically, are
understood to be implemented by computer programs or equivalent
electrical circuits, microcode, or the like. Furthermore, it has
also proven convenient at times, to refer to these arrangements of
operations as modules, without loss of generality. The described
operations and their associated modules may be embodied in
software, firmware, hardware, or any combinations thereof.
[0050] Any of the steps, operations, or processes described herein
may be performed or implemented with one or more hardware or
software modules, alone or in combination with other devices. In
one embodiment, a software module is implemented with a computer
program product comprising a computer-readable non-transitory
medium containing computer program code, which can be executed by a
computer processor for performing any or all of the steps,
operations, or processes described.
[0051] Embodiments of the invention may also relate to an apparatus
for performing the operations herein. This apparatus may be
specially constructed for the required purposes, and/or it may
comprise a general-purpose computing device selectively activated
or reconfigured by a computer program stored in the computer. Such
a computer program may be stored in a non-transitory, tangible
computer readable storage medium, or any type of media suitable for
storing electronic instructions, which may be coupled to a computer
system bus. Furthermore, any computing systems referred to in the
specification may include a single processor or may be
architectures employing multiple processor designs for increased
computing capability.
[0052] Embodiments of the invention may also relate to a product
that is produced by a computing process described herein. Such a
product may comprise information resulting from a computing
process, where the information is stored on a non-transitory,
tangible computer readable storage medium and may include any
embodiment of a computer program product or other data combination
described herein.
[0053] Finally, the language used in the specification has been
principally selected for readability and instructional purposes,
and it may not have been selected to delineate or circumscribe the
inventive subject matter. It is therefore intended that the scope
of the invention be limited not by this detailed description, but
rather by any claims that issue on an application based hereon.
Accordingly, the disclosure of the embodiments of the invention is
intended to be illustrative, but not limiting, of the scope of the
invention, which is set forth in the following claims.
* * * * *