Patent Publication Number: US-2020275848-A1

Title: Virtual reality guided meditation with biofeedback

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     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 
     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 
     Meditation can provide numerous physical and mental benefits. For example, on a physical level, meditation may increase a person&#39;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. 
     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 
     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&#39;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&#39;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&#39;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. 
     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 
         FIG. 1  is a block diagram of a computing environment for guided meditation with a VR guided meditation system according to one embodiment. 
         FIG. 2  is a block diagram of the VR guided meditation system within the computing environment of  FIG. 1  according to one embodiment. 
         FIG. 3A  is a user interface illustrating heart rate biofeedback according to one embodiment. 
         FIG. 3B  is a user interface illustrating meditation performance biofeedback according to one embodiment. 
         FIG. 3C  is a user interface illustrating biofeedback trends according to one embodiment. 
         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. 
         FIG. 5  is a flow chart illustrating a process for providing guided meditation according to one embodiment. 
     
    
    
     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 
       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 . 
     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. 
     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®, FITBIT FLEX™, or JAWBONE UP4™. The health data source  120  may also be a third party application, e.g., APPLE® HEALTHKIT, GARMIN®, GOOGLE® FIT, UNDER ARMOUR® MAPMYFITNESS, and STRAVA®. 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&#39;s chest, arm, or leg, a headband or head device worn on the user&#39;s head, among other options. The physiological state information includes, e.g., the user&#39;s heart rate, the user&#39;s blood pressure, the user&#39;s activity (e.g., number of steps walked, number of miles ran, number of calories burned, etc.), the user&#39;s breathing patterns, the user&#39;s sleep activity, the user&#39;s brain waves, the user&#39;s pain response, the user&#39;s skin conductance, the user&#39;s eye movements, the user&#39;s muscle action potentials, the user&#39;s temperature, the user&#39;s skin electrical activity, or the user&#39;s blood flow, among other types of health data. 
     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®, WiFi, ZIGBEE®, 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. 
     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&#39;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 . 
       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. 
     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 . 
     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 . 
     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 . 
     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.” 
     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 . 
     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&#39;s account data in the user account store  240 , the health data source manager  220  determines whether the user&#39;s account is already associated with the health data sources  120 . If the user&#39;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&#39;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). 
     In one embodiment, the heath data sources  120  include a FITBIT® fitness tracker device and an APPLE® 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® (third-party) application. If the FITBIT® application authenticates the login credentials, then the health data source manager  220  is authorized to retrieve information from an account of the FITBIT® 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® (third-party) application. If the APPLE® application authenticates the login credentials, then the health data source manager  220  is authorized to retrieve information from an account of the APPLE® 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&#39;s current heart rate information to the health data source manager  220  once every hour. 
     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&#39;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. 
     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&#39;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. 
     In some embodiments, the biofeedback module  225  generates a report of a user&#39;s biofeedback. The report can provide various data about the user and one or more changes in the user&#39;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&#39;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&#39;s weight has decreased within two months of starting guided meditation exercises. The report might include information about the user&#39;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&#39;s sleep patterns relative to those of a population of users performing the same type of meditation exercise. 
     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&#39;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&#39;s physiological state or health metrics. The report provided to the doctor may include the user&#39;s own data and/or the user&#39;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. 
     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&#39;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&#39;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. 
     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&#39;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&#39;s physiological state. For example, based on previous VR guided meditation exercises performed by the user, the user&#39;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&#39;s biofeedback to update the user&#39;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. 
     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&#39;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. 
     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&#39; 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&#39;s physiological state) meditation exercise recommendations compared to a general recommendation model  230  for an entire set of users. 
     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 . 
     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&#39;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 
       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&#39;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&#39;s heart rate in bpm. In the embodiment shown in  FIG. 3A , the graph  304  indicates that user&#39;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&#39;s heart rate before starting the VR guided meditation exercise is 80 bpm and that the user&#39;s heart rate after starting the VR guided meditation exercise is 62 bpm, e.g., corresponding to the information shown in the graph  304 . 
       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&#39;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&#39;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&#39;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. 
     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. 
       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&#39;s biofeedback trends associated with different parameters. In particular, statistic  334  indicates that the user&#39;s average heart rate while meditating in the morning is 74 bpm. Since 74 bpm is lower than the user&#39;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&#39;s average heart rate while performing “body scan” type VR guided meditation exercises is 60 bpm. Since 60 bpm is lower than the user&#39;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&#39;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&#39;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. 
     The statistic  340  indicates the user&#39;s sleep activity biofeedback associated with VR guided meditation exercises performed by the user. In particular, the statistic  340  indicates that the user&#39;s average duration of sleep on days that the user meditated is 7.2 hours, and that the user&#39;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&#39;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 
       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&#39;s post-exercise heart rate is 10 bpm lower than the user&#39;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.” 
     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 
       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 . 
     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. 
     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&#39;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&#39;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 
     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. 
     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. 
     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. 
     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. 
     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. 
     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.