PATENT DOCUMENT

Publication Number: US-12002140-B1
Application Number: US-202318200540-A
Country: US
Kind Code: B1

Title: Method and device for surfacing accumulated strain information

Abstract:
A method includes: presenting a posture summary interface including: a representation of the user, a visualization of a current accumulated strain value for the user, and a first affordance for initiating an animated posture summary associated with the accumulated strain value for the user over a respective time window; and in response to detecting a user input directed to the first affordance within the posture summary interface, presenting an animation of the representation of the user over the respective time window that corresponds to one or more instances in which head pose information changes associated with the user caused an increase or a decrease to the accumulated strain value greater than a significance threshold, wherein an appearance of the visualization of the current accumulated strain value for the user changes to represent the accumulated strain value for the user over the respective time window.

Claims:
What is claimed is: 
     
       1. A method comprising:
 at a computing system including non-transitory memory and one or more processors, wherein the computing system is communicatively coupled to a display device and one or more input devices via a communication interface: 
 presenting, via the display device, a notification that corresponds to a posture summary for a user of the computing system; 
 detecting, via the one or more input devices, a user input directed to the notification; 
 in response to detecting the user input directed to the notification, presenting, via the display device, a posture summary interface including: a representation of the user, a visualization of a current accumulated strain value for the user, and a first affordance for initiating an animated posture summary associated with the accumulated strain value for the user over a respective time window; 
 detecting, via the one or more input devices, a user input directed to the first affordance; and 
 in response to detecting the user input directed to the first affordance, presenting, via the display device, an animation of the representation of the user over the respective time window that corresponds to one or more instances in which head pose information changes associated with the user caused an increase or a decrease to the accumulated strain value greater than a significance threshold, wherein an appearance of the visualization of the current accumulated strain value for the user changes to represent the accumulated strain value for the user over the respective time window. 
 
     
     
       2. The method of  claim 1 , wherein the representation of the user is animated according to the head pose information changes during the one or more instances within the respective time window. 
     
     
       3. The method of  claim 1 , wherein the representation of the user corresponds to an avatar with a face modeled on the user. 
     
     
       4. The method of  claim 1 , wherein the visualization of the current accumulated strain value for the user corresponds to a colored gradient, wherein a wavelength of the colored gradient is based on the current accumulated strain value. 
     
     
       5. The method of  claim 1 , wherein the respective time window corresponds to a deterministic or non-deterministic length of time. 
     
     
       6. The method of  claim 1 , wherein the user input directed to the notification corresponds to one of a touch input directed to the display device, a voice input, a gaze input, or a hand/extremity tracking input. 
     
     
       7. The method of  claim 1 , wherein the posture summary interface includes a second affordance for presenting a detailed accumulated strain value interface associated with the accumulated strain value for the user over at least the respective time window. 
     
     
       8. The method of  claim 1 , further comprising:
 while presenting the animation of the representation of the user over the respective time window, presenting, via the display device, a second affordance for presenting a detailed accumulated strain value interface associated with the accumulated strain value for the user over at least the respective time window and a third affordance for initiating a stretching session, wherein an appearance of the second and third affordances change to represent the accumulated strain value for the user over the respective time window. 
 
     
     
       9. The method of any of  claim 8 , wherein the detailed accumulated strain value interface illustrates at least one of: a graph of the accumulated strain value over at least the respective time window or current strain values for individual muscles or muscle groups/regions that comprise the accumulated strain value. 
     
     
       10. The method of  claim 8 , wherein selection of the third affordance causes initiation of a stretching session to ameliorate the accumulated strain value. 
     
     
       11. The method of  claim 1 , wherein the computing system surfaces the notification when the respective time window elapses. 
     
     
       12. A device comprising:
 one or more processors; 
 a non-transitory memory; 
 an interface for communicating with a display device and one or more input devices; and 
 one or more programs stored in the non-transitory memory, which, when executed by the one or more processors, cause the device to:
 present, via the display device, a notification that corresponds to a posture summary for a user of the computing system; 
 detect, via the one or more input devices, a user input directed to the notification; 
 in response to detecting the user input directed to the notification, present, via the display device, a posture summary interface including: a representation of the user, a visualization of a current accumulated strain value for the user, and a first affordance for initiating an animated posture summary associated with the accumulated strain value for the user over a respective time window; 
 detect, via the one or more input devices, a user input directed to the first affordance; and 
 in response to detecting the user input directed to the first affordance, present, via the display device, an animation of the representation of the user over the respective time window that corresponds to one or more instances in which head pose information changes associated with the user caused an increase or a decrease to the accumulated strain value greater than a significance threshold, wherein an appearance of the visualization of the current accumulated strain value for the user changes to represent the accumulated strain value for the user over the respective time window. 
 
 
     
     
       13. The device of  claim 12 , wherein the representation of the user is animated according to the head pose information changes during the one or more instances within the respective time window. 
     
     
       14. The device of  claim 12 , wherein the representation of the user corresponds to an avatar with a face modeled on the user. 
     
     
       15. The device of  claim 12 , wherein the visualization of the current accumulated strain value for the user corresponds to a colored gradient, wherein a wavelength of the colored gradient is based on the current accumulated strain value. 
     
     
       16. A non-transitory memory storing one or more programs, which, when executed by one or more processors of a device with an interface for communicating with a display device and one or more input devices, cause the device to:
 present, via the display device, a notification that corresponds to a posture summary for a user of the computing system; 
 detect, via the one or more input devices, a user input directed to the notification; 
 in response to detecting the user input directed to the notification, present, via the display device, a posture summary interface including: a representation of the user, a visualization of a current accumulated strain value for the user, and a first affordance for initiating an animated posture summary associated with the accumulated strain value for the user over a respective time window; 
 detect, via the one or more input devices, a user input directed to the first affordance; and 
 in response to detecting the user input directed to the first affordance, present, via the display device, an animation of the representation of the user over the respective time window that corresponds to one or more instances in which head pose information changes associated with the user caused an increase or a decrease to the accumulated strain value greater than a significance threshold, wherein an appearance of the visualization of the current accumulated strain value for the user changes to represent the accumulated strain value for the user over the respective time window. 
 
     
     
       17. The non-transitory memory of  claim 16 , wherein the representation of the user is animated according to the head pose information changes during the one or more instances within the respective time window. 
     
     
       18. The non-transitory memory of  claim 16 , wherein the representation of the user corresponds to an avatar with a face modeled on the user. 
     
     
       19. The non-transitory memory of  claim 16 , wherein the visualization of the current accumulated strain value for the user corresponds to a colored gradient, wherein a wavelength of the colored gradient is based on the current accumulated strain value.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is claims priority to U.S. Provisional Patent App. No. 63/344,738, filed on May 23, 2022, which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure generally relates to posture awareness and, in particular, to systems, devices, and methods for surfacing accumulated strain information. 
     BACKGROUND 
     Many persons may spend a significant number of hours at their computers or other devices during both work and non-work hours. This time spent using a computer or other devices may negatively impact the posture of said person. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       So that the present disclosure can be understood by those of ordinary skill in the art, a more detailed description may be had by reference to aspects of some illustrative implementations, some of which are shown in the accompanying drawings. 
         FIG.  1    is a block diagram of an example operating architecture in accordance with some implementations. 
         FIG.  2    is a block diagram of an example controller in accordance with some implementations. 
         FIG.  3    is a block diagram of an example electronic device in accordance with some implementations. 
         FIG.  4 A  is a block diagram of a first portion of a data processing architecture in accordance with some implementations. 
         FIG.  4 B  illustrates example data structures in accordance with some implementations. 
         FIG.  4 C  is a block diagram of a second portion of a data processing architecture in accordance with some implementations. 
         FIG.  4 D  illustrates example data structures in accordance with some implementations. 
         FIG.  4 E  is a block diagram of a third portion of a data processing architecture in accordance with some implementations. 
         FIG.  5    is a block diagram of an example content delivery architecture in accordance with some implementations. 
         FIGS.  6 A- 6 H  illustrate a plurality of interfaces associated with surfacing accumulated strain information in accordance with some implementations. 
         FIGS.  7 A and  7 B  illustrate a flowchart representation of a method of surfacing accumulated strain information in accordance with some implementations. 
     
    
    
     In accordance with common practice the various features illustrated in the drawings may not be drawn to scale. Accordingly, the dimensions of the various features may be arbitrarily expanded or reduced for clarity. In addition, some of the drawings may not depict all of the components of a given system, method, or device. Finally, like reference numerals may be used to denote like features throughout the specification and figures. 
     SUMMARY 
     Various implementations disclosed herein include devices, systems, and methods for surfacing accumulated strain information. According to some implementations, the method is performed at a computing system including non-transitory memory and one or more processors, wherein the computing system is communicatively coupled to a display device and one or more input devices. The method includes: presenting, via the display device, a posture summary interface including: a representation of the user, a visualization of a current accumulated strain value for the user, and a first affordance for initiating an animated posture summary associated with the accumulated strain value for the user over a respective time window; detecting a user input, via the one or more input devices, directed to the first affordance within the posture summary interface; and in response to detecting the user input directed to the first affordance within the posture summary interface, presenting, via the display device, an animation of the representation of the user over the respective time window that corresponds to one or more instances in which head pose information changes associated with the user caused an increase or a decrease to the accumulated strain value greater than a significance threshold, wherein an appearance of the visualization of the current accumulated strain value for the user changes to represent the accumulated strain value for the user over the respective time window. 
     In accordance with some implementations, an electronic device includes one or more displays, one or more processors, a non-transitory memory, and one or more programs; the one or more programs are stored in the non-transitory memory and configured to be executed by the one or more processors and the one or more programs include instructions for performing or causing performance of any of the methods described herein. In accordance with some implementations, a non-transitory computer readable storage medium has stored therein instructions, which, when executed by one or more processors of a device, cause the device to perform or cause performance of any of the methods described herein. In accordance with some implementations, a device includes: one or more displays, one or more processors, a non-transitory memory, and means for performing or causing performance of any of the methods described herein. 
     In accordance with some implementations, a computing system includes one or more processors, non-transitory memory, an interface for communicating with a display device and one or more input devices, and one or more programs; the one or more programs are stored in the non-transitory memory and configured to be executed by the one or more processors and the one or more programs include instructions for performing or causing performance of the operations of any of the methods described herein. In accordance with some implementations, a non-transitory computer readable storage medium has stored therein instructions which when executed by one or more processors of a computing system with an interface for communicating with a display device and one or more input devices, cause the computing system to perform or cause performance of the operations of any of the methods described herein. In accordance with some implementations, a computing system includes one or more processors, non-transitory memory, an interface for communicating with a display device and one or more input devices, and means for performing or causing performance of the operations of any of the methods described herein. 
     DESCRIPTION 
     Numerous details are described in order to provide a thorough understanding of the example implementations shown in the drawings. However, the drawings merely show some example aspects of the present disclosure and are therefore not to be considered limiting. Those of ordinary skill in the art will appreciate that other effective aspects and/or variants do not include all of the specific details described herein. Moreover, well-known systems, methods, components, devices, and circuits have not been described in exhaustive detail so as not to obscure more pertinent aspects of the example implementations described herein. 
       FIG.  1    is a block diagram of an example operating architecture  100  in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the operating architecture  100  includes an optional controller  110  and an electronic device  120  (e.g., a tablet, mobile phone, laptop, near-eye system, wearable computing device, or the like). 
     In some implementations, the controller  110  is configured to manage and coordinate an extended reality (XR) experience (sometimes also referred to herein as a “XR environment” or a “virtual environment” or a “graphical environment”) for a user  150  and optionally other users. In some implementations, the controller  110  includes a suitable combination of software, firmware, and/or hardware. The controller  110  is described in greater detail below with respect to  FIG.  2   . In some implementations, the controller  110  is a computing device that is local or remote relative to the physical environment  105 . For example, the controller  110  is a local server located within the physical environment  105 . In another example, the controller  110  is a remote server located outside of the physical environment  105  (e.g., a cloud server, central server, etc.). In some implementations, the controller  110  is communicatively coupled with the electronic device  120  via one or more wired or wireless communication channels  144  (e.g., BLUETOOTH, IEEE 802.11x, IEEE 802.16x, IEEE 802.3x, etc.). In some implementations, the functions of the controller  110  are provided by the electronic device  120 . As such, in some implementations, the components of the controller  110  are integrated into the electronic device  120 . 
     In some implementations, the electronic device  120  is configured to present audio and/or video (A/V) content to the user  150 . In some implementations, the electronic device  120  is configured to present a user interface (UI) and/or an XR environment  128  to the user  150 . In some implementations, the electronic device  120  includes a suitable combination of software, firmware, and/or hardware. The electronic device  120  is described in greater detail below with respect to  FIG.  3   . 
     According to some implementations, the electronic device  120  presents an XR experience to the user  150  while the user  150  is physically present within a physical environment  105  that includes a table  107  within the field-of-view (FOV)  111  of the electronic device  120 . As such, in some implementations, the user  150  holds the electronic device  120  in his/her hand(s). In some implementations, while presenting the XR experience, the electronic device  120  is configured to present XR content (sometimes also referred to herein as “graphical content” or “virtual content”), including an XR cylinder  109 , and to enable video pass-through of the physical environment  105  (e.g., including the table  107  or a representation thereof) on a display  122 . For example, the XR environment  128 , including the XR cylinder  109 , is volumetric or three-dimensional (3D). 
     In one example, the XR cylinder  109  corresponds to head/display-locked content such that the XR cylinder  109  remains displayed at the same location on the display  122  as the FOV  111  changes due to translational and/or rotational movement of the electronic device  120 . As another example, the XR cylinder  109  corresponds to world/object-locked content such that the XR cylinder  109  remains displayed at its origin location as the FOV  111  changes due to translational and/or rotational movement of the electronic device  120 . As such, in this example, if the FOV  111  does not include the origin location, the displayed XR environment  128  will not include the XR cylinder  109 . As another example, the XR cylinder  109  corresponds to body-locked content such that it remains at a positional and rotational offset from the body of the user  150 . In some examples, the electronic device  120  corresponds to a near-eye system, mobile phone, tablet, laptop, wearable computing device, or the like. 
     In some implementations, the display  122  corresponds to an additive display that enables optical see-through of the physical environment  105  including the table  107 . For example, the display  122  corresponds to a transparent lens, and the electronic device  120  corresponds to a pair of glasses worn by the user  150 . As such, in some implementations, the electronic device  120  presents a user interface by projecting the XR content (e.g., the XR cylinder  109 ) onto the additive display, which is, in turn, overlaid on the physical environment  105  from the perspective of the user  150 . In some implementations, the electronic device  120  presents the user interface by displaying the XR content (e.g., the XR cylinder  109 ) on the additive display, which is, in turn, overlaid on the physical environment  105  from the perspective of the user  150 . 
     In some implementations, the user  150  wears the electronic device  120  such as a near-eye system. As such, the electronic device  120  includes one or more displays for displaying the XR content (e.g., a single display or one for each eye). For example, the electronic device  120  encloses the FOV of the user  150 . In such implementations, the electronic device  120  presents the XR environment  128  by displaying data corresponding to the XR environment  128  on the one or more displays or by projecting data corresponding to the XR environment  128  onto the retinas of the user  150 . 
     In some implementations, the electronic device  120  includes an integrated display (e.g., a built-in display) that displays the XR environment  128 . In some implementations, the electronic device  120  includes a head-mountable enclosure. In various implementations, the head-mountable enclosure includes an attachment region to which another device with a display can be attached. For example, in some implementations, the electronic device  120  can be attached to the head-mountable enclosure. In various implementations, the head-mountable enclosure is shaped to form a receptacle for receiving another device that includes a display (e.g., the electronic device  120 ). For example, in some implementations, the electronic device  120  slides/snaps into or otherwise attaches to the head-mountable enclosure. In some implementations, the display of the device attached to the head-mountable enclosure presents (e.g., displays) the XR environment  128 . In some implementations, the electronic device  120  is replaced with an XR chamber, enclosure, or room configured to present XR content in which the user  150  does not wear the electronic device  120 . 
     In some implementations, the controller  110  and/or the electronic device  120  cause an XR representation of the user  150  to move within the XR environment  128  based on movement information (e.g., body pose data, eye tracking data, hand/limb/finger/extremity tracking data, etc.) from the electronic device  120  and/or optional remote input devices within the physical environment  105 . In some implementations, the optional remote input devices correspond to fixed or movable sensory equipment within the physical environment  105  (e.g., image sensors, depth sensors, infrared (IR) sensors, event cameras, microphones, etc.). In some implementations, each of the remote input devices is configured to collect/capture input data and provide the input data to the controller  110  and/or the electronic device  120  while the user  150  is physically within the physical environment  105 . In some implementations, the remote input devices include microphones, and the input data includes audio data associated with the user  150  (e.g., speech samples). In some implementations, the remote input devices include image sensors (e.g., cameras), and the input data includes images of the user  150 . In some implementations, the input data characterizes body poses of the user  150  at different times. In some implementations, the input data characterizes head poses of the user  150  at different times. In some implementations, the input data characterizes hand tracking information associated with the hands of the user  150  at different times. In some implementations, the input data characterizes the velocity and/or acceleration of body parts of the user  150  such as his/her hands. In some implementations, the input data indicates joint positions and/or joint orientations of the user  150 . In some implementations, the remote input devices include feedback devices such as speakers, lights, or the like. 
       FIG.  2    is a block diagram of an example of the controller  110  in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, the controller  110  includes one or more processing units  202  (e.g., microprocessors, application-specific integrated-circuits (ASICs), field-programmable gate arrays (FPGAs), graphics processing units (GPUs), central processing units (CPUs), processing cores, and/or the like), one or more input/output (I/O) devices  206 , one or more communication interfaces  208  (e.g., universal serial bus (USB), IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, global system for mobile communications (GSM), code division multiple access (CDMA), time division multiple access (TDMA), global positioning system (GPS), infrared (IR), BLUETOOTH, ZIGBEE, and/or the like type interface), one or more programming (e.g., I/O) interfaces  210 , a memory  220 , and one or more communication buses  204  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  204  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices  206  include at least one of a keyboard, a mouse, a touchpad, a touchscreen, a joystick, one or more microphones, one or more speakers, one or more image sensors, one or more displays, and/or the like. 
     The memory  220  includes high-speed random-access memory, such as dynamic random-access memory (DRAM), static random-access memory (SRAM), double-data-rate random-access memory (DDR RAM), or other random-access solid-state memory devices. In some implementations, the memory  220  includes non-volatile memory such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory  220  optionally includes one or more storage devices remotely located from the one or more processing units  202 . The memory  220  comprises a non-transitory computer readable storage medium. In some implementations, the memory  220  or the non-transitory computer readable storage medium of the memory  220  stores the following programs, modules and data structures, or a subset thereof described below with respect to  FIG.  2   . 
     An operating system  230  includes procedures for handling various basic system services and for performing hardware dependent tasks. 
     In some implementations, a data obtainer  242  is configured to obtain data (e.g., captured image frames of the physical environment  105 , presentation data, input data, user interaction data, camera pose tracking information, eye tracking information, head/body pose tracking information, hand/limb/finger/extremity tracking information, sensor data, location data, etc.) from at least one of the I/O devices  206  of the controller  110 , the I/O devices and sensors  306  of the electronic device  120 , and the optional remote input devices. To that end, in various implementations, the data obtainer  242  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a mapper and locator engine  244  is configured to map the physical environment  105  and to track the position/location of at least the electronic device  120  or the user  150  with respect to the physical environment  105 . To that end, in various implementations, the mapper and locator engine  244  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a data transmitter  246  is configured to transmit data (e.g., presentation data such as rendered image frames associated with the XR environment, location data, etc.) to at least the electronic device  120  and optionally one or more other devices. To that end, in various implementations, the data transmitter  246  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a privacy architecture  408  is configured to ingest data and filter user information and/or identifying information within the data based on one or more privacy filters. The privacy architecture  408  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the privacy architecture  408  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a motion state estimator  410  is configured to obtain (e.g., receive, retrieve, or determine/generate) a motion state vector  411  associated with the electronic device  120  (and the user  150 ) (e.g., including a current motion state associated with the electronic device  120 ) based on input data and update the motion state vector  411  over time. For example, as shown in  FIG.  4 B , the motion state vector  411  includes a motion state descriptor  422  for the electronic device  120  (e.g., stationary, in-motion, walking, running, cycling, operating or riding in an automobile car, operating or riding in a boat, operating or riding in a bus, operating or riding in a train, operating or riding in an aircraft, or the like), translational movement values  424  associated with the electronic device  120  (e.g., a heading, a velocity value, an acceleration value, etc.), angular movement values  426  associated with the electronic device  120  (e.g., an angular velocity value, an angular acceleration value, and/or the like for each of the pitch, roll, and yaw dimensions), and/or the like. The motion state estimator  410  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the motion state estimator  410  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, an eye tracking engine  412  is configured to obtain (e.g., receive, retrieve, or determine/generate) an eye tracking vector  413  as shown in  FIG.  4 B  (e.g., with a gaze direction) based on the input data and update the eye tracking vector  413  over time. For example, the gaze direction indicates a point (e.g., associated with x, y, and z coordinates relative to the physical environment  105  or the world-at-large), a physical object, or a region of interest (ROI) in the physical environment  105  at which the user  150  is currently looking. As another example, the gaze direction indicates a point (e.g., associated with x, y, and z coordinates relative to the XR environment  128 ), an XR object, or a ROI in the XR environment  128  at which the user  150  is currently looking. The eye tracking engine  412  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the eye tracking engine  412  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a head/body pose tracking engine  414  is configured to obtain (e.g., receive, retrieve, or determine/generate) a pose characterization vector  415  based on the input data and update the pose characterization vector  415  over time. For example, as shown in  FIG.  4 B , the pose characterization vector  415  includes a head pose descriptor  442 A (e.g., upward, downward, neutral, etc.), translational values  442 B for the head pose, rotational values  442 C for the head pose, a body pose descriptor  444 A (e.g., standing, sitting, prone, etc.), translational values  444 B for body sections/extremities/limbs/joints, rotational values  444 C for the body sections/extremities/limbs/joints, and/or the like. The head/body pose tracking engine  414  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the head/body pose tracking engine  414  includes instructions and/or logic therefor, and heuristics and metadata therefor. In some implementations, the motion state estimator  410 , the eye tracking engine  412 , and the head/body pose tracking engine  414  may be located on the electronic device  120  in addition to or in place of the controller  110 . 
     In some implementations, a content selector  522  is configured to select XR content (sometimes also referred to herein as “graphical content” or “virtual content”) from a content library  525  based on one or more user requests and/or inputs (e.g., a voice command, a selection from a user interface (UI) menu of XR content items or virtual agents (VAs), and/or the like). The content selector  522  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the content selector  522  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a content library  525  includes a plurality of content items such as audio/visual (A/V) content, virtual agents (VAs), and/or XR content, objects, items, scenery, etc. As one example, the XR content includes 3D reconstructions of user captured videos, movies, TV episodes, and/or other XR content. In some implementations, the content library  525  is pre-populated or manually authored by the user  150 . In some implementations, the content library  525  is located local relative to the controller  110 . In some implementations, the content library  525  is located remote from the controller  110  (e.g., at a remote server, a cloud server, or the like). 
     In some implementations, a characterization engine  416  is configured to determine/generate a characterization vector  419  based on at least one of the motion state vector  411 , the eye tracking vector  413 , and the pose characterization vector  415  as shown in  FIG.  4 A . In some implementations, the characterization engine  416  is also configured to update the pose characterization vector  419  over time. As shown in  FIG.  4 B , the characterization vector  419  includes motion state information  452 , gaze direction information  454 , head pose information  456 A, body pose information  456 AB, extremity tracking information  456 AC, location information  458 , and/or the like. The characterization engine  416  is described in more detail below with reference to  FIG.  4 A . To that end, in various implementations, the characterization engine  416  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a context analyzer  460  is configured to obtain (e.g., receive, retrieve, or determine/generate) a context information vector  470  based on input data shown in  FIG.  4 C  and update the context information vector  470  over time. As shown in  FIG.  4 C , the context information vector  470  includes environmental state information  472 , device state information  474 , and user state information  476 . The context analyzer  460  is described in more detail below with reference to  FIG.  4 C . To that end, in various implementations, the context analyzer  460  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a muscle strain engine  463  is configured to obtain (e.g., receive, retrieve, or determine/generate) current strain information  480  based on input data shown in  FIG.  4 C  and update the current strain information  480  over time. As shown in  FIG.  4 D , the current strain information  480  includes: muscle information  482 A associated with a first muscle or muscle group/region; muscle information  482 B associated with a second muscle or muscle group/region; muscle information  482 C associated with a third muscle or muscle group/region; muscle information  482 D associated with a fourth muscle or muscle group/region; muscle information  482 E associated with a fifth muscle or muscle group/region; and current accumulated strain information  486 . To that end, in various implementations, the muscle strain engine  463  includes a head/body/neck mechanics engine  462  and a strain analyzer  464  with strain increase logic  465 A and strain decrease logic  465 B. The muscle strain engine  463  is described in more detail below with reference to  FIG.  4 C . To that end, in various implementations, the muscle strain engine  463  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a posture summary engine  468 A is configured to generate and surface a notification that corresponds to a posture summary for a user. In some implementations, the posture summary engine  468 A is also configured to generate accumulated strain visualizations  469  including an animation of a representation of the user over the respective time window that corresponds to one or more instances in which head pose information changes associated with the user caused an increase or a decrease to the accumulated strain value greater than a significance threshold. The posture summary engine  468 A is described in more detail below with reference to  FIG.  4 E . To that end, in various implementations, the posture summary engine  468 A includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, an application programing interface (API)  468 B is configured to provide access to the current strain information  480  to at least one of the operating system of the controller  110 , the electronic device  120 , or a combination thereof; third-party programs or applications; and/or the like. As such, the current strain information  480  may be used in various downstream processes. The API  468 B is described in more detail below with reference to  FIG.  4 E . To that end, in various implementations, the API  468 B includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a content manager  530  is configured to manage and update the layout, setup, structure, and/or the like for the XR environment  128  including one or more of VA(s), XR content, one or more user interface (UI) elements associated with the XR content, and/or the like. The content manager  530  is described in more detail below with reference to  FIG.  5   . To that end, in various implementations, the content manager  530  includes instructions and/or logic therefor, and heuristics and metadata therefor. In some implementations, the content manager  530  includes a frame buffer  532 , a content updater  534 , and a feedback engine  536 . In some implementations, the frame buffer  532  includes XR content, a rendered image frame, and/or the like for one or more past instances and/or frames. 
     In some implementations, the content updater  534  is configured to modify the XR environment  128  over time based on translational or rotational movement of the electronic device  120  or physical objects within the physical environment  105 , user inputs (e.g., a change in context, hand/extremity tracking inputs, eye tracking inputs, touch inputs, voice commands, modification/manipulation inputs with the physical object, and/or the like), and/or the like. To that end, in various implementations, the content updater  534  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the feedback engine  536  is configured to generate sensory feedback (e.g., visual feedback such as text or lighting changes, audio feedback, haptic feedback, etc.) associated with the XR environment  128 . To that end, in various implementations, the feedback engine  536  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, a rendering engine  550  is configured to render a graphical user interface (GUI) or an XR environment  128  (sometimes also referred to herein as a “graphical environment” or “virtual environment”) or image frame associated therewith as well as accumulated strain visualizations  469 , VA(s), XR content, one or more UI elements associated with the XR content, and/or the like. To that end, in various implementations, the rendering engine  550  includes instructions and/or logic therefor, and heuristics and metadata therefor. In some implementations, the rendering engine  550  includes a pose determiner  552 , a renderer  554 , an optional image processing architecture  556 , and an optional compositor  558 . One of ordinary skill in the art will appreciate that the optional image processing architecture  556  and the optional compositor  558  may be present for video pass-through configurations but may be removed for fully VR or optical see-through configurations. 
     In some implementations, the pose determiner  552  is configured to determine a current camera pose of the electronic device  120  and/or the user  150  relative to the A/V content and/or XR content. The pose determiner  552  is described in more detail below with reference to  FIG.  5   . To that end, in various implementations, the pose determiner  552  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the renderer  554  is configured to render the accumulated strain visualizations  469 . In some implementations, the renderer  554  is configured to render the A/V content and/or the XR content according to the current camera pose relative thereto. The renderer  554  is described in more detail below with reference to  FIG.  5   . To that end, in various implementations, the renderer  554  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the image processing architecture  556  is configured to obtain (e.g., receive, retrieve, or capture) an image stream including one or more images of the physical environment  105  from the current camera pose of the electronic device  120  and/or the user  150 . In some implementations, the image processing architecture  556  is also configured to perform one or more image processing operations on the image stream such as warping, color correction, gamma correction, sharpening, noise reduction, white balance, and/or the like. The image processing architecture  556  is described in more detail below with reference to  FIG.  5   . To that end, in various implementations, the image processing architecture  556  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the compositor  558  is configured to composite the rendered A/V content and/or XR content with the processed image stream of the physical environment  105  from the image processing architecture  556  to produce rendered image frames of the XR environment  128  for display. The compositor  558  is described in more detail below with reference to  FIG.  5   . To that end, in various implementations, the compositor  558  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtainer  242 , the mapper and locator engine  244 , the data transmitter  246 , the privacy architecture  408 , the motion state estimator  410 , the eye tracking engine  412 , the head/body pose tracking engine  414 , the characterization engine  416 , the context analyzer  460 , the muscle strain engine  463 , the posture summary engine  468 A, the API  468 B, the content selector  522 , the content manager  530 , and the rendering engine  550  are shown as residing on a single device (e.g., the controller  110 ), it should be understood that in other implementations, any combination of the data obtainer  242 , the mapper and locator engine  244 , the data transmitter  246 , the privacy architecture  408 , the motion state estimator  410 , the eye tracking engine  412 , the head/body pose tracking engine  414 , the characterization engine  416 , the context analyzer  460 , the muscle strain engine  463 , the posture summary engine  468 A, the API  468 B, the content selector  522 , the content manager  530 , and the rendering engine  550  may be located in separate computing devices. 
     In some implementations, the functions and/or components of the controller  110  are combined with or provided by the electronic device  120  shown below in  FIG.  3   . Moreover,  FIG.  2    is intended more as a functional description of the various features which may be present in a particular implementation as opposed to a structural schematic of the implementations described herein. As recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some functional modules shown separately in  FIG.  2    could be implemented in a single module and the various functions of single functional blocks could be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of particular functions and how features are allocated among them will vary from one implementation to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular implementation. 
       FIG.  3    is a block diagram of an example of the electronic device  120  (e.g., a mobile phone, tablet, laptop, near-eye system, wearable computing device, or the like) in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, the electronic device  120  includes one or more processing units  302  (e.g., microprocessors, ASICs, FPGAs, GPUs, CPUs, processing cores, and/or the like), one or more input/output (I/O) devices and sensors  306 , one or more communication interfaces  308  (e.g., USB, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, GSM, CDMA, TDMA, GPS, IR, BLUETOOTH, ZIGBEE, and/or the like type interface), one or more programming (e.g., I/O) interfaces  310 , one or more displays  312 , an image capture device  370  (e.g., one or more optional interior-facing and/or exterior-facing image sensors), a memory  320 , and one or more communication buses  304  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  304  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices and sensors  306  include at least one of an inertial measurement unit (IMU), an accelerometer, a gyroscope, a magnetometer, a thermometer, one or more physiological sensors (e.g., blood pressure monitor, heart rate monitor, blood oximetry monitor, blood glucose monitor, etc.), one or more microphones, one or more speakers, a haptics engine, a heating and/or cooling unit, a skin shear engine, one or more depth sensors (e.g., structured light, time-of-flight, LiDAR, or the like), a localization and mapping engine, an eye tracking engine, a head/body pose tracking engine, a hand/limb/finger/extremity tracking engine, a camera pose tracking engine, and/or the like. 
     In some implementations, the one or more displays  312  are configured to present the XR environment to the user. In some implementations, the one or more displays  312  are also configured to present flat video content to the user (e.g., a 2-dimensional or “flat” AVI, FLV, WMV, MOV, MP4, or the like file associated with a TV episode or a movie, or live video pass-through of the physical environment  105 ). In some implementations, the one or more displays  312  correspond to touchscreen displays (e.g., similar to the display  122  in  FIG.  1   ). In some implementations, the one or more displays  312  correspond to holographic, digital light processing (DLP), liquid-crystal display (LCD), liquid-crystal on silicon (LCoS), organic light-emitting field-effect transitory (OLET), organic light-emitting diode (OLED), surface-conduction electron-emitter display (SED), field-emission display (FED), quantum-dot light-emitting diode (QD-LED), micro-electro-mechanical system (MEMS), and/or the like display types. In some implementations, the one or more displays  312  correspond to diffractive, reflective, polarized, holographic, etc. waveguide displays. For example, the electronic device  120  includes a single display such as the display  122 . In another example, the electronic device  120  includes a display for each eye of the user. In some implementations, the one or more displays  312  are capable of presenting AR and VR content. In some implementations, the one or more displays  312  are capable of presenting AR or VR content. 
     In some implementations, the image capture device  370  correspond to one or more RGB cameras (e.g., with a complementary metal-oxide-semiconductor (CMOS) image sensor or a charge-coupled device (CCD) image sensor), IR image sensors, event-based cameras, and/or the like. In some implementations, the image capture device  370  includes a lens assembly, a photodiode, and a front-end architecture. In some implementations, the image capture device  370  includes exterior-facing and/or interior-facing image sensors. 
     The memory  320  includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices. In some implementations, the memory  320  includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory  320  optionally includes one or more storage devices remotely located from the one or more processing units  302 . The memory  320  comprises a non-transitory computer readable storage medium. In some implementations, the memory  320  or the non-transitory computer readable storage medium of the memory  320  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  330  and a presentation engine  340 . 
     The operating system  330  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the presentation engine  340  is configured to present media items and/or XR content to the user via the one or more displays  312 . To that end, in various implementations, the presentation engine  340  includes a data obtainer  342 , an interaction handler  520 , a presenter  560 , and a data transmitter  350 . 
     In some implementations, the data obtainer  342  is configured to obtain data (e.g., presentation data such as rendered image frames associated with the user interface or the XR environment, input data, user interaction data, head tracking information, camera pose tracking information, eye tracking information, hand/limb/finger/extremity tracking information, sensor data, location data, etc.) from at least one of the I/O devices and sensors  306  of the electronic device  120 , the controller  110 , and the remote input devices. To that end, in various implementations, the data obtainer  342  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the interaction handler  520  is configured to detect user interactions (e.g., gestural inputs detected via hand/extremity tracking, eye gaze inputs detected via eye tracking, voice commands, etc.) with the presented A/V content and/or XR content. To that end, in various implementations, the interaction handler  520  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the presenter  560  is configured to present and update A/V content and/or XR content (e.g., the rendered image frames associated with the GUI or the XR environment  128  including the accumulated strain visualizations  469 , VA(s), the XR content, one or more UI elements associated with the XR content, and/or the like) via the one or more displays  312 . To that end, in various implementations, the presenter  560  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitter  350  is configured to transmit data (e.g., presentation data, location data, user interaction data, head tracking information, camera pose tracking information, eye tracking information, hand/limb/finger/extremity tracking information, etc.) to at least the controller  110 . To that end, in various implementations, the data transmitter  350  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtainer  342 , the interaction handler  520 , the presenter  560 , and the data transmitter  350  are shown as residing on a single device (e.g., the electronic device  120 ), it should be understood that in other implementations, any combination of the data obtainer  342 , the interaction handler  520 , the presenter  560 , and the data transmitter  350  may be located in separate computing devices. 
     Moreover,  FIG.  3    is intended more as a functional description of the various features which may be present in a particular implementation as opposed to a structural schematic of the implementations described herein. As recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some functional modules shown separately in  FIG.  3    could be implemented in a single module and the various functions of single functional blocks could be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of particular functions and how features are allocated among them will vary from one implementation to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular implementation. 
       FIG.  4 A  is a block diagram of a first portion  400 A of an example data processing architecture in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the first portion  400 A of the data processing architecture is included in a computing system such as the controller  110  shown in  FIGS.  1  and  2   ; the electronic device  120  shown in  FIGS.  1  and  3   ; and/or a suitable combination thereof. 
     As shown in  FIG.  4 A , one or more local sensors  402  of the controller  110 , the electronic device  120 , and/or a combination thereof obtain local sensor data  403  associated with the physical environment  105 . For example, the local sensor data  403  includes images or a stream thereof of the physical environment  105 , simultaneous location and mapping (SLAM) information for the physical environment  105  and the location of the electronic device  120  or the user  150  relative to the physical environment  105 , ambient lighting information for the physical environment  105 , ambient audio information for the physical environment  105 , acoustic information for the physical environment  105 , dimensional information for the physical environment  105 , semantic labels for objects within the physical environment  105 , and/or the like. In some implementations, the local sensor data  403  includes un-processed or post-processed information. 
     Similarly, as shown in  FIG.  4 A , one or more remote sensors  404  associated with the optional remote input devices within the physical environment  105  obtain remote sensor data  405  associated with the physical environment  105 . For example, the remote sensor data  405  includes images or a stream thereof of the physical environment  105 , SLAM information for the physical environment  105  and the location of the electronic device  120  or the user  150  relative to the physical environment  105 , ambient lighting information for the physical environment  105 , ambient audio information for the physical environment  105 , acoustic information for the physical environment  105 , dimensional information for the physical environment  105 , semantic labels for objects within the physical environment  105 , and/or the like. In some implementations, the remote sensor data  405  includes un-processed or post-processed information. 
     According to some implementations, the privacy architecture  408  ingests the local sensor data  403  and the remote sensor data  405 . In some implementations, the privacy architecture  408  includes one or more privacy filters associated with user information and/or identifying information. In some implementations, the privacy architecture  408  includes an opt-in feature where the electronic device  120  informs the user  150  as to what user information and/or identifying information is being monitored and how the user information and/or the identifying information will be used. In some implementations, the privacy architecture  408  selectively prevents and/or limits the data processing architecture  400 A/ 400 B/ 400 C or portions thereof from obtaining and/or transmitting the user information. To this end, the privacy architecture  408  receives user preferences and/or selections from the user  150  in response to prompting the user  150  for the same. In some implementations, the privacy architecture  408  prevents the data processing architecture  400 A/ 400 B/ 400 C from obtaining and/or transmitting the user information unless and until the privacy architecture  408  obtains informed consent from the user  150 . In some implementations, the privacy architecture  408  anonymizes (e.g., scrambles, obscures, encrypts, and/or the like) certain types of user information. For example, the privacy architecture  408  receives user inputs designating which types of user information the privacy architecture  408  anonymizes. As another example, the privacy architecture  408  anonymizes certain types of user information likely to include sensitive and/or identifying information, independent of user designation (e.g., automatically). 
     According to some implementations, the motion state estimator  410  obtains the local sensor data  403  and the remote sensor data  405  after it has been subjected to the privacy architecture  408 . In some implementations, the motion state estimator  410  obtains (e.g., receives, retrieves, or determines/generates) a motion state vector  411  based on the input data and updates the motion state vector  411  over time. 
       FIG.  4 B  shows an example data structure for the motion state vector  411  in accordance with some implementations. As shown in  FIG.  4 B , the motion state vector  411  may correspond to an N-tuple characterization vector or characterization tensor that includes a timestamp  421  (e.g., the most recent time the motion state vector  411  was updated), a motion state descriptor  422  for the electronic device  120  (e.g., stationary, in-motion, running, walking, cycling, driving or riding in a car, driving or riding in a boat, driving or riding in a bus, riding in a train, riding in a plane, or the like), translational movement values  424  associated with the electronic device  120  (e.g., a heading, a displacement value, a velocity value, an acceleration value, a jerk value, etc.), angular movement values  426  associated with the electronic device  120  (e.g., an angular displacement value, an angular velocity value, an angular acceleration value, an angular jerk value, and/or the like for each of the pitch, roll, and yaw dimensions), and/or miscellaneous information  428 . One of ordinary skill in the art will appreciate that the data structure for the motion state vector  411  in  FIG.  4 B  is merely an example that may include different information portions in various other implementations and be structured in myriad ways in various other implementations. 
     According to some implementations, the eye tracking engine  412  obtains the local sensor data  403  and the remote sensor data  405  after it has been subjected to the privacy architecture  408 . In some implementations, the eye tracking engine  412  obtains (e.g., receives, retrieves, or determines/generates) an eye tracking vector  413  based on the input data and updates the eye tracking vector  413  over time. 
       FIG.  4 B  shows an example data structure for the eye tracking vector  413  in accordance with some implementations. As shown in  FIG.  4 B , the eye tracking vector  413  may correspond to an N-tuple characterization vector or characterization tensor that includes a timestamp  431  (e.g., the most recent time the eye tracking vector  413  was updated), one or more angular values  432  for a current gaze direction (e.g., roll, pitch, and yaw values), one or more translational values  434  for the current gaze direction (e.g., x, y, and z values relative to the physical environment  105 , the world-at-large, and/or the like), and/or miscellaneous information  436 . One of ordinary skill in the art will appreciate that the data structure for the eye tracking vector  413  in  FIG.  4 B  is merely an example that may include different information portions in various other implementations and be structured in myriad ways in various other implementations. 
     For example, the gaze direction indicates a point (e.g., associated with x, y, and z coordinates relative to the physical environment  105  or the world-at-large), a physical object, or a region of interest (ROI) in the physical environment  105  at which the user  150  is currently looking. As another example, the gaze direction indicates a point (e.g., associated with x, y, and z coordinates relative to the XR environment  128 ), an XR object, or a region of interest (ROI) in the XR environment  128  at which the user  150  is currently looking. 
     According to some implementations, the head/body pose tracking engine  414  obtains the local sensor data  403  and the remote sensor data  405  after it has been subjected to the privacy architecture  408 . In some implementations, the head/body pose tracking engine  414  obtains (e.g., receives, retrieves, or determines/generates) a pose characterization vector  415  based on the input data and updates the pose characterization vector  415  over time. 
       FIG.  4 B  shows an example data structure for the pose characterization vector  415  in accordance with some implementations. As shown in  FIG.  4 B , the pose characterization vector  415  may correspond to an N-tuple characterization vector or characterization tensor that includes a timestamp  441  (e.g., the most recent time the pose characterization vector  415  was updated), a head pose descriptor  442 A (e.g., upward, downward, neutral, etc.), translational values for the head pose  442 B, rotational values for the head pose  442 C, a body pose descriptor  444 A (e.g., standing, sitting, prone, etc.), translational values for body sections/extremities/limbs/joints  444 B, rotational values for the body sections/extremities/limbs/joints  444 C, and/or miscellaneous information  446 . In some implementations, the pose characterization vector  415  also includes information associated with finger/hand/extremity tracking. One of ordinary skill in the art will appreciate that the data structure for the pose characterization vector  415  in  FIG.  4 B  is merely an example that may include different information portions in various other implementations and be structured in myriad ways in various other implementations. According to some implementations, the motion state vector  411 , the eye tracking vector  413  and the pose characterization vector  415  are collectively referred to as an input vector  417 . 
     According to some implementations, the characterization engine  416  obtains the motion state vector  411 , the eye tracking vector  413  and the pose characterization vector  415 . In some implementations, the characterization engine  416  obtains (e.g., receives, retrieves, or determines/generates) the characterization vector  419  based on the motion state vector  411 , the eye tracking vector  413 , and the pose characterization vector  415 . 
       FIG.  4 B  shows an example data structure for the characterization vector  419  in accordance with some implementations. As shown in  FIG.  4 B , the characterization vector  419  may correspond to an N-tuple characterization vector or characterization tensor that includes a timestamp  451  (e.g., the most recent time the characterization vector  419  was updated), motion state information  452  (e.g., the motion state descriptor  422 ), gaze direction information  454  (e.g., a function of the one or more angular values  432  and the one or more translational values  434  within the eye tracking vector  413 ), head pose information  456 A (e.g., a function of the head pose descriptor  442 A within the pose characterization vector  415 ), body pose information  456 B (e.g., a function of the body pose descriptor  444 A within the pose characterization vector  415 ), extremity tracking information  456 C (e.g., a function of the body pose descriptor  444 A within the pose characterization vector  415  that is associated with extremities of the user  150  that are being tracked by the controller  110 , the electronic device  120 , and/or a combination thereof), location information  458  (e.g., a household location such as a kitchen or living room, a vehicular location such as an automobile, plane, etc., and/or the like), and/or miscellaneous information  459 . 
       FIG.  4 C  is a block diagram of a second portion  400 B of the example data processing architecture in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the second portion  400 B of the data processing architecture is included in a computing system such as the controller  110  shown in  FIGS.  1  and  2   ; the electronic device  120  shown in  FIGS.  1  and  3   ; and/or a suitable combination thereof.  FIG.  4 C  is similar to and adapted from  FIG.  4 A . Therefore, similar reference numbers are used in  FIGS.  4 A and  4 C . As such, only the differences between  FIGS.  4 A and  4 C  will be described below for the sake of brevity. 
     According to some implementations, the context analyzer  460  obtains the motion state vector  411  from the motion state estimator  410 . As shown in  FIG.  4 C , the context analyzer  460  also obtains the local sensor data  403  and the remote sensor data  405  after being subjected to the privacy architecture  408 . 
     In some implementations, the context analyzer  460  obtains (e.g., receives, retrieves, or determines/generates) a context information vector  470  based on the input data and updates the context information vector  470  over time.  FIG.  4 C  shows an example data structure for the context information vector  470  in accordance with some implementations. As shown in  FIG.  4 C , the context information vector  470  may correspond to an N-tuple characterization vector or characterization tensor that includes: a timestamp  471  (e.g., the most recent time the context information vector  470  was updated); environmental state information  472  associated with a current state of the physical environment  105  (e.g., ambient temperature information, ambient humidity information, ambient lighting information, ambient audio information, semantic labels for physical objects within the physical environment  105 , locations for physical objects within the physical environment  105 , etc.); device state information  474  associated with a current state of the controller  110 , the electronic device  120 , or a combination thereof, or the like (e.g., current foreground applications, current background applications, power/charge remaining, device temperature metrics, resource consumption metrics (e.g., CPU, RAM, storage, network I/O, etc.), etc.); user state information  476  associated with a current state of the user  150  (e.g., the characterization vector  419 , physiological information associated with the user  150 , the motion state descriptor  42 , etc.); and miscellaneous information  478 . One of ordinary skill in the art will appreciate that the data structure for the context information vector  470  in  FIG.  4 C  is merely an example that may include different information portions in various other implementations and be structured in myriad ways in various other implementations. 
     According to some implementations, the head/body/neck mechanics engine  462  obtains (e.g., receives, retrieves, or determines/generates) displacement, velocity, acceleration, jerk, torque, etc. values for the head/body/neck of the user  150  based on changes to the pose characterization vector  415 . In some implementations, the strain analyzer  464  determines current strain information  480  for one or more muscles or muscle groups based on: the displacement, velocity, acceleration, jerk, torque, etc. values for the head/body/neck of the user  150  from the head/body/neck mechanics engine  462 ; historical information  466 ; and the context information vector  470 . In some implementations, the strain analyzer  464  determines the current strain information  480  based on strain increase logic  465 A and/or strain decrease logic  465 B. In some implementations, the historical information  466  corresponds to local or remote storage repository, including: strain information for one or more previous time periods on an overall basis, individual muscle or muscle group/region basis, etc.; context information for one or more previous time periods; and/or displacement, velocity, acceleration, jerk, torque, etc. values for the head/body/neck of the user  150  for one or more previous time periods. 
       FIG.  4 D  shows an example data structure for the current strain information  480  in accordance with some implementations. As shown in  FIG.  4 C , the current strain information  480  may correspond to an N-tuple characterization vector or characterization tensor that includes: a timestamp  481 ; muscle information  482 A associated with a first muscle or muscle group/region; muscle information  482 B associated with a second muscle or muscle group/region; muscle information  482 C associated with a third muscle or muscle group/region; muscle information  482 D associated with a fourth muscle or muscle group/region; muscle information  482 E associated with a fifth muscle or muscle group/region; accumulated strain information  486  associated with a function of the muscle information  482 A- 482 E; and miscellaneous information  488 . One of ordinary skill in the art will appreciate that the data structure for current strain information  480  in  FIG.  4 D  is merely an example that may include different information portions in various other implementations and be structured in myriad ways in various other implementations. 
     As shown in  FIG.  4 D , the muscle information  482 A for the first muscle or muscle group/region includes: a muscle identifier  484 A for the first muscle or muscle group/region (e.g., a unique identifier, a label, a name, or the like for the first muscle or muscle group/region); a current muscle strain value  485 A for the first muscle or muscle group/region; a pointer to historical muscle strain information  486 A for the first muscle or muscle group/region within the historical information  466 ; and miscellaneous information  487 A associated with the first muscle or muscle group/region. 
     As shown in  FIG.  4 D , for example, the muscle strain engine  463  determines current muscle strain values  485 A,  485 B,  485 C,  485 D, and  485 E for muscles or muscle groups/regions  484 A,  484 B,  484 C,  484 D, and  484 E, respectively, of the user  150 . Furthermore, the muscle strain engine  463  updates (increases or decreases) the muscle strain values  485 A,  485 B,  485 C,  485 D, and  485 E over time based on rotational and/or translational movement of the user  150  that triggers the strain increase logic  465 A and/or the strain decrease logic  465 B. 
     As shown in  FIG.  4 D , the accumulated strain information  486 : a current accumulated strain value  489 A associated with a function of one or more of the current muscle strain values  485 A,  485 B,  485 C,  485 D, and  485 E for muscles or muscle groups/regions  484 A,  484 B,  484 C,  484 D, and  484 E, respectively, of the user  150 ; a pointer to historical accumulated strain information  489 B within the historical information  466 ; and miscellaneous information  489 C associated with the accumulated strain information  486 . As shown in  FIG.  4 D , for example, the muscle strain engine  463  also determines a current accumulated strain value  489 A and updates (increases or decreases) the current accumulated strain value  489 A over time based on rotational and/or translational movement of the user  150  that triggers the strain increase logic  465 A and/or the strain decrease logic  465 B. As such, according to some implementations, the muscle strain engine  463  tracks strain values on an individual muscle or muscle group/region basis (e.g., the muscle information  482 A- 482 E) as well as an overall strain value (e.g., the current accumulated strain information  486 ). 
       FIG.  4 E  is a block diagram of a third portion  400 C of the example data processing architecture in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the third portion  400 C of the example data processing architecture is included in a computing system such as the controller  110  shown in  FIGS.  1  and  2   ; the electronic device  120  shown in  FIGS.  1  and  3   ; and/or a suitable combination thereof.  FIG.  4 E  is similar to and adapted from  FIGS.  4 A and  4 C . Therefore, similar reference numbers are used in  FIGS.  4 A and  4 C . As such, only the differences between  FIGS.  4 A,  4 C, and  4 E  will be described below for the sake of brevity. 
     As described above with respect to  FIG.  4 C , the muscle strain engine  463  determines a current strain information  480 . As illustrated in  FIG.  4 E , the current strain information  480  is provided to a multiplexer (Mux)  467 . In turn, the current strain information  480  is provided to at least one of a posture summary engine  468 A and an application programming interface (API)  468 B. According to some implementations, the posture summary engine  468 A generates and surfaces a notification that corresponds to a posture summary for a user. In some implementations, the posture summary engine  468 A also generates accumulated strain visualizations  469  including an animation of a representation of the user over the respective time window that corresponds to one or more instances in which head pose information changes associated with the user caused an increase or a decrease to the accumulated strain value greater than a significance threshold. The animation of a representation of the user over the respective time window is described in greater detail below with reference to  FIGS.  6 C- 6 F . According to some implementations, the API  468 B provides access to the current strain information  480  to at least one of: the operating system of the controller  110 , the electronic device  120 , or a combination thereof, third-party programs or applications; and/or the like. As such, the current strain information  480  may be used in various downstream processes. 
       FIG.  5    is a block diagram of an example content delivery architecture  500  in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the content delivery architecture is included in a computing system such as the controller  110  shown in  FIGS.  1  and  2   ; the electronic device  120  shown in  FIGS.  1  and  3   ; and/or a suitable combination thereof. 
     According to some implementations, the interaction handler  520  obtains (e.g., receives, retrieves, or detects) one or more user inputs  521  provided by the user  150  that are associated with selecting A/V content, one or more VAs, and/or XR content for presentation. For example, the one or more user inputs  521  correspond to a gestural input selecting XR content from a UI menu detected via hand/extremity tracking, an eye gaze input selecting XR content from the UI menu detected via eye tracking, a voice command selecting XR content from the UI menu detected via a microphone, and/or the like. In some implementations, the content selector  522  selects XR content  527  from the content library  525  based on one or more user inputs  521  (e.g., a voice command, a selection from a menu of XR content items, and/or the like). 
     In various implementations, the content manager  530  manages and updates the layout, setup, structure, and/or the like for the XR environment  128 , including one or more of VAs, XR content, one or more UI elements associated with the XR content, and/or the like, based on the characterization vector  419 , (optionally) the user inputs  521 , and/or the like. To that end, the content manager  530  includes the frame buffer  532 , the content updater  534 , and the feedback engine  536 . 
     In some implementations, the frame buffer  532  includes XR content, a rendered image frame, and/or the like for one or more past instances and/or frames. In some implementations, the content updater  534  modifies the XR environment  128  over time based on the characterization vector  419 , the accumulated strain visualizations  469 , the user inputs  521  associated with modifying and/or manipulating the XR content or VA(s), translational or rotational movement of objects within the physical environment  105 , translational or rotational movement of the electronic device  120  (or the user  150 ), and/or the like. In some implementations, the feedback engine  536  generates sensory feedback (e.g., visual feedback such as text or lighting changes, audio feedback, haptic feedback, etc.) associated with the XR environment  128 . 
     According to some implementations, the pose determiner  552  determines a current camera pose of the electronic device  120  and/or the user  150  relative to the XR environment  128  and/or the physical environment  105  based at least in part on the pose characterization vector  415 . In some implementations, the renderer  554  renders the VA(s), the XR content  527 , one or more UI elements associated with the XR content, and/or the like according to the current camera pose relative thereto. 
     According to some implementations, the optional image processing architecture  556  obtains an image stream from an image capture device  370  including one or more images of the physical environment  105  from the current camera pose of the electronic device  120  and/or the user  150 . In some implementations, the image processing architecture  556  also performs one or more image processing operations on the image stream such as warping, color correction, gamma correction, sharpening, noise reduction, white balance, and/or the like. In some implementations, the optional compositor  558  composites the rendered XR content with the processed image stream of the physical environment  105  from the image processing architecture  556  to produce rendered image frames of the XR environment  128 . In various implementations, the presenter  560  presents the rendered image frames of the XR environment  128  to the user  150  via the one or more displays  312 . One of ordinary skill in the art will appreciate that the optional image processing architecture  556  and the optional compositor  558  may not be applicable for fully virtual environments (or optical see-through scenarios). 
       FIGS.  6 A- 6 H  illustrate a plurality of interfaces associated with surfacing accumulated strain information in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, the plurality of interfaces is rendered and presented by a computing system such as the controller  110  shown in  FIGS.  1  and  2   ; the electronic device  120  shown in  FIGS.  1  and  3   ; and/or a suitable combination thereof. 
     In some implementations, the plurality of interfaces in  FIGS.  6 A- 6 H  correspond to two-dimensional (2D) graphical user interfaces (GUIs) or three-dimensional (3D) environments. In some implementations, the plurality of interfaces in  FIGS.  6 A- 6 H  corresponds to the XR environment  128  shown in  FIG.  1    (e.g., a 3D or volumetric user interface). As such, according to some implementations, the electronic device  120  presents the XR environment  128  to the user  150  while the user  150  is physically present within a physical environment, which is currently within the FOV  111  of an exterior-facing image sensor of the electronic device  120  (e.g., as shown in  FIG.  1   ). In other words, in some implementations, the electronic device  120  is configured to present XR content (e.g., virtual content) and to enable optical see-through or video pass-through of at least a portion of the physical environment on the display  122 . For example, the electronic device  120  corresponds to a mobile phone, tablet, laptop, near-eye system, wearable computing device, or the like. As such, in some implementations, the user  150  holds the electronic device  120  in their hand(s) similar to the operating environment  100  in  FIG.  1   . 
     As shown in  FIG.  6 A , the electronic device  120  presents a home interface  605  including a plurality of application icons and a plurality of dock application icons on the display  122 . In  FIG.  6 A , the electronic device  120  overlays a notification  602  on the home interface  605  that corresponds to a posture summary for a user of the electronic device  120  based on accumulated muscle strain. For example, with reference to  FIG.  4 D , the accumulated muscle strain is based on the strain information  480 , which includes a current accumulated strain value  489 A associated with a function of one or more of the current muscle strain values  485 A,  485 B,  485 C,  485 D, and  485 E for muscles or muscle groups/regions  484 A,  484 B,  484 C,  484 D, and  484 E, respectively. For example, the electronic device  120  presents the notification  602  at a default location or in a default manner in  FIG.  6 A  (e.g., a pop-up notification centered within the display  122 , a banner notification adjacent to the top edge of the display  122 , or the like). 
     For example, the notification  602  corresponds to 2D content or volumetric/3D virtual content. According to some implementations, the notification  602  acts as an affordance that may be selected with a touch input, hand tracking input, gaze input, voice command, or the like. In some implementations, the electronic device  120  surfaces the notification  602  once a day, twice a day, at a user-specified time, when the accumulated strain value reaches a deterministic or non-deterministic value, or the like. 
     As shown in  FIG.  6 A , the electronic device  120  detects a first user input  604  (e.g., tap input, touch input, or the like) directed to the notification  602  (e.g., via the display  122 —a touchscreen). As shown in  FIG.  6 B , in response to detecting the first user input  604  directed to the notification  602  in  FIG.  6 A , the electronic device  120  presents a posture summary interface  615  on the display. One of ordinary skill in the art will appreciate that the posture summary interface  615  may be formatted or structured in myriad ways in various other implementations. 
     As shown in  FIG.  6 B , the posture summary interface  615  includes a representation of the user  617  such as an avatar with a face modeled on the user of the electronic device  120 . As shown in  FIG.  6 B , the posture summary interface  615  also includes a visualization of the accumulated strain  621  for the user of the electronic device  120  with a current accumulated strain value indicator  624 A. As mentioned above, the accumulated (muscle) strain is based on the strain information  480 , which includes a current accumulated strain value  489 A (e.g., associated with the current accumulated strain value indicator  624 A) corresponding to a function of one or more of the current muscle strain values  485 A,  485 B,  485 C,  485 D, and  485 E for muscles or muscle groups/regions  484 A,  484 B,  484 C,  484 D, and  484 E, respectively, of the user  150  (e.g., as shown in  FIG.  4 D ). 
     For example, the visualization of the accumulated strain  621  corresponds to a colored gradient, wherein the wavelength of the colored gradient is based on the magnitude of the current accumulated strain value indicator  624 A. According to some implementations, the wavelength of the colored gradient changes from green to red as the accumulated neck strain increases. In some implementations, the opposite occurs as the accumulated neck strain decreases. 
     As shown in  FIG.  6 B , the posture summary interface  615  further includes a first affordance  612  for initiating an animated posture summary associated with the accumulated strain for the user over a respective time window. One of ordinary skill in the art will appreciate that the animated posture summary may be presented or structured in myriad ways in various other implementations. In some implementations, the respective time window corresponds to a deterministic or non-deterministic length of time such as the last week, the last X days, the last day, the last Y hours, the last hour, the last Z minutes, workday, workout session, or the like. As such, for example, each of the T 1 -T 6  indicators shown on the timeline in  FIGS.  6 C- 6 F  corresponds to a day, Y hours, one hour, Z minutes, or the like. 
     For example, the first affordance  612  may be selected with a touch input, hand tracking input, gaze input, voice command, or the like. As shown in  FIG.  6 B , the posture summary interface  615  further includes a second affordance  619  for presenting a detailed accumulated strain interface associated with the accumulated strain value for the user over at least the respective time window (e.g., shown in  FIG.  6 H ). For example, the second affordance  619  may be selected with a touch input, hand tracking input, gaze input, voice command, or the like. 
     As shown in  FIG.  6 B , the electronic device  120  detects a second user input  622  (e.g., tap input, touch input, or the like) directed to the first affordance  612  (e.g., via the display  122 —a touchscreen). As shown in  FIG.  6 C , in response to detecting the second user input  622  directed to the first affordance  612  in  FIG.  6 B , the electronic device  120  presents an animation of the representation of the user  617  over the respective time window that corresponds to one or more instances in which head pose information changes (and/or body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than a significance threshold. 
     As shown in  FIG.  6 C , in response to detecting the second user input  622  directed to the first affordance  612  in  FIG.  6 B , the electronic device  120  ceases display of the first affordance  612  and presents a timeline with a time indicator  634 A. For example, the time indicator  634 A corresponds to a first instance among the one or more instances in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold. 
     Furthermore, as shown in  FIG.  6 C , the visualization of the accumulated strain  621  for the user of the electronic device  120  includes a current accumulated strain value indicator  632 A for the time indicator  634 A, and the visualization of the accumulated strain  621  is associated with a first appearance (e.g., white) based on the magnitude of the current accumulated strain value indicator  632 A. In some implementations, the appearance of the visualization of the accumulated strain  621  changes based on the current magnitude accumulated of the strain value indicator for the current time indicator. 
     In some implementations, the significance threshold is deterministic or non-deterministic based on the number of identified instances in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold. For example, the significance threshold changes such that only N instances are shown by the animation. In some implementations, the significance threshold is deterministic or non-deterministic based on user preferences, user history, or the like. For example, the significance threshold may be higher for a user associated with frequent large variances in the accumulated strain value, whereas the significance threshold may be lower for a user associated with smaller variances in the accumulated strain value. As such, the one or more instances are selected as “highlights” to be viewed by the user. However, the user may scrub to any time on the timeline by, for example, dragging the current time indicator or providing a time via a voice command. 
     As shown in  FIG.  6 C , in response to detecting the second user input  622  directed to the first affordance  612  in  FIG.  6 B , the electronic device  120  also presents a third affordance  631  for initiating a stretching session. In some implementations, an appearance of the second affordance  619  and the third affordance  631  change to represent the accumulated strain value for the user over the respective time window. For example, the appearance of the second affordance  619  and the third affordance  631  correspond to the first appearance (e.g., white) associated with the visualization of the accumulated strain  621 . As such, the appearance of the second affordance  619  and the third affordance  631  also change based on the current magnitude accumulated of the strain value indicator for the current time indicator. 
     As shown in  FIG.  6 D , the electronic device  120  continues presentation of the animation of the representation of the user  617  over the respective time window that corresponds to one or more instances in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold. As shown in  FIG.  6 D , the electronic device  120  presents the timeline with a time indicator  634 B that corresponds to a second instance among the one or more instances in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold. 
     Furthermore, as shown in  FIG.  6 D , the visualization of the accumulated strain  621  for the user of the electronic device  120  includes a current accumulated strain value indicator  632 B for the time indicator  634 B, and the visualization of the accumulated strain  621  is associated with a second appearance (e.g., light grey) based on the magnitude of the current accumulated strain value indicator  632 B that is greater than the magnitude of the accumulated strain value indicator  632 A in  FIG.  6 C . According to some implementations, the representation of the user  617  is animated according to the head pose information changes (and/or the body pose information changes) during the one or more instances within the respective time window (e.g., the second instance in  FIG.  6 D ). 
     As shown in  FIG.  6 E , the electronic device  120  continues presentation of the animation of the representation of the user  617  over the respective time window that corresponds to one or more instances in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold. As shown in  FIG.  6 E , the electronic device  120  presents the timeline with a time indicator  634 C that corresponds to a third instance among the one or more instances in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold. 
     Furthermore, as shown in  FIG.  6 E , the visualization of the accumulated strain  621  for the user of the electronic device  120  includes a current accumulated strain value indicator  632 C for the time indicator  634 C, and the visualization of the accumulated strain  621  is associated with a third appearance (e.g., dark grey) based on the magnitude of the current accumulated strain value indicator  632 C that is greater than the magnitude of the accumulated strain value indicator  632 B in  FIG.  6 D . According to some implementations, the representation of the user  617  is animated according to the head pose information changes (and/or the body pose information changes) during the one or more instances within the respective time window (e.g., the third instance in  FIG.  6 E ). 
     As shown in  FIG.  6 F , the electronic device  120  continues presentation of the animation of the representation of the user  617  over the respective time window that corresponds to one or more instances in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold. As shown in  FIG.  6 F , the electronic device  120  presents the timeline with a time indicator  634 D that corresponds to a fourth instance among the one or more instances in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold. 
     Furthermore, as shown in  FIG.  6 F , the visualization of the accumulated strain  621  for the user of the electronic device  120  includes a current accumulated strain value indicator  632 D for the time indicator  634 D, and the visualization of the accumulated strain  621  is associated with a fourth appearance (e.g., black) based on the magnitude of the current accumulated strain value indicator  632 D that is greater than the magnitude of the accumulated strain value indicator  632 C in  FIG.  6 E . According to some implementations, the representation of the user  617  is animated according to the head pose information changes (and/or the body pose information changes) during the one or more instances within the respective time window (e.g., the fourth instance in  FIG.  6 F ). 
     As shown in  FIG.  6 F , the electronic device  120  detects a third user input  650  (e.g., tap input, touch input, or the like) directed to the second affordance  619  (e.g., via the display  122 —a touchscreen). As shown in  FIG.  6 G , in response to detecting the third user input  650  directed to the second affordance  619  in  FIG.  6 F , the electronic device  120  presents a detailed accumulated strain interface  655 . 
     As shown in  FIG.  6 G , the detailed accumulated strain interface  655  includes a graph  656  of the accumulated strain value over at least the respective time window, where the endpoint  658  corresponds to the magnitude of the current accumulated strain value indicator  624 A in  FIG.  6 B . Furthermore, as shown in  FIG.  6 G , the detailed accumulated strain interface  655  also includes an illustration of the muscles or muscle groups/regions  484 A,  484 B,  484 C,  484 D, and  484 E of the user  150  as well as current accumulated muscle strain values  485 A,  485 B,  485 C,  485 D, and  485 E for the muscles or muscle groups/regions  484 A,  484 B,  484 C,  484 D, and  484 E, respectively. One of ordinary skill in the art will appreciate that the detailed accumulated strain interface  655  may be formatted or structured in myriad ways in various other implementations. 
     As shown in  FIG.  6 G , the electronic device  120  detects a fourth user input  652  (e.g., tap input, touch input, or the like) directed to the muscle or muscle group/region  484 E (e.g., via the display  122 —a touchscreen). As shown in  FIG.  6 H , in response to detecting the fourth user input  652  directed to the muscle or muscle group/region  484 E in  FIG.  6 G , the electronic device  120  presents a detailed muscle strain interface  665  for the muscle or muscle group/region  484 E. As shown in  FIG.  6 H , the detailed muscle strain interface  665  includes a graph  668  of the muscle strain value  485 E over at least the respective time window, where the endpoint corresponds to the current muscle strain value  485 E. As such, the user may drill down into muscle or muscle group/region specific information from the detailed accumulated strain interface  655  in  FIG.  6 G . One of ordinary skill in the art will appreciate that the detailed muscle strain interface  665  may be formatted or structured in myriad ways in various other implementations. 
       FIGS.  7 A and  7 B  illustrate a flowchart representation of a method  700  of surfacing accumulated strain information with some implementations. In various implementations, the method  700  is performed at a computing system including non-transitory memory and one or more processors, wherein the computing system is communicatively coupled to a display device and one or more input devices (e.g., the electronic device  120  shown in  FIGS.  1  and  3   ; the controller  110  in  FIGS.  1  and  2   ; or a suitable combination thereof). In some implementations, the method  700  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  700  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). In some implementations, the computing system corresponds to one of a tablet, a laptop, a mobile phone, a near-eye system, a wearable computing device, or the like. In some implementations, the one or more input devices correspond to a computer vision (CV) engine that uses an image stream from one or more exterior-facing image sensors, a finger/hand/extremity tracking engine, an eye tracking engine, a touch-sensitive surface, one or more microphones, and/or the like. 
     As discussed above, many persons (e.g., the user  150  in  FIG.  1   ) may spend a significant number of hours at their computers or other devices during both work and non-work hours. This time spent using a computer or other devices may negatively impact the posture of said person. As such, described herein is a method and device for promoting posture awareness by surfacing accumulated strain information. 
     According to some implementations, as represented by block  702 , the method  700  includes presenting, via the display device, a notification that corresponds to a posture summary for a user of the computing system. In some implementations, the computing system overlays the notification on current content such as a two-dimensional (2D) graphical user interface (GUI) or a three-dimensional (3D) environment. In some implementations, the computing system composites the notification with the current content such as the 3D environment. In some implementations, the notification corresponds to 2D content or volumetric/3D virtual content. In some implementations, the notification acts as an affordance that may be selected with a touch input, hand tracking input, gaze input, voice command, or the like. 
     As one example, with reference to  FIG.  6 A , the electronic device  120  presents a notification  602  overlaid on a home interface  605 , wherein the notification  602  corresponds to a posture summary for a user of the electronic device  120  based on accumulated muscle strain. For example, with reference to  FIG.  5   , the computing system or a portion thereof (e.g., the rendering engine  550 ) renders one or more images frames for a GUI or an XR environment (e.g., a 3D environment), and the computing system or a portion thereof (e.g., the presenter  560 ) presents the rendered image frames for the GUI or the XR environment to the user  150  via the one or more displays  312 . In some implementations, the display device corresponds to a transparent lens assembly, and wherein presenting the XR environment includes projecting at least a portion of the XR environment onto the transparent lens assembly. In some implementations, the display device corresponds to a near-eye system, and wherein presenting the XR environment includes compositing at least a portion of the XR environment with one or more images of a physical environment captured by an exterior-facing image sensor. 
     According to some implementations, the computing system surfaces the notification when the respective time window, which is mentioned below with respect to block  706 , elapses. In some implementations, the computing system surfaces the notification once a day, twice a day, at a user-specified time, when the accumulated strain value reaches a deterministic or non-deterministic value, or the like. 
     According to some implementations, as represented by block  704 , the method  700  includes detecting a previous user input, via the one or more input devices, directed to the notification. In some implementations, the first user input corresponds to one of a touch input directed to the display device, a voice input, a gaze input, or a hand/extremity tracking input. As one example, with reference to  FIG.  6 A , the electronic device  120  detects a first user input  604  (e.g., tap input, touch input, or the like) directed to the notification  602  (e.g., via the display  122 —a touchscreen). 
     As represented by block  706 , the method  700  includes presenting, via the display device, a posture summary interface including: a representation of the user, a visualization of a current accumulated strain value for the user, and a first affordance for initiating an animated posture summary associated with the accumulated strain value for the user over a respective time window. In some implementations, the respective time window corresponds to a deterministic or non-deterministic length of time such as the last week, the last X days, the last day, the last Y hours, the last hour, the last Z minutes, workday, workout session, or the like. According to some implementations, the computing system presents the posture summary interface in response to detecting the previous input directed to the notification as described in block  704 . According to some implementations, the computing system presents the posture summary interface when the respective time window elapses. According to some implementations, the computing system presents the posture summary interface once a day, twice a day, at a user-specified time, when the accumulated strain value reaches a deterministic or non-deterministic value, or the like. 
     As one example, with reference to  FIG.  6 B , in response to detecting the first user input  604  directed to the notification  602  in  FIG.  6 A , the electronic device  120  presents a posture summary interface  615  on the display. Continuing with this example, the posture summary interface  615  in  FIG.  6 B  includes a representation of the user  617  such as an avatar modeled on the face and/or body of the user of the electronic device  120 . Continuing with this example, the posture summary interface  615  in  FIG.  6 B  also includes a visualization of the accumulated strain  621  for the user of the electronic device  120  with a current accumulated strain value indicator  624 A. According to some implementations, the accumulated (muscle) strain is based on the strain information  480 , which includes a current accumulated strain value  489 A (e.g., associated with the current accumulated strain value indicator  624 A) corresponding to a function of one or more of the current muscle strain values  485 A,  485 B,  485 C,  485 D, and  485 E for muscles or muscle groups/regions  484 A,  484 B,  484 C,  484 D, and  484 E, respectively, of the user  150  (e.g., as shown in  FIG.  4 D ). Continuing with this example, the posture summary interface  615  in  FIG.  6 B  further includes a first affordance  612  for initiating an animated posture summary associated with the accumulated strain for the user over a respective time window. For example, the first affordance  612  may be selected with a touch input, hand tracking input, gaze input, voice command, or the like. 
     In some implementations, as represented by block  708 , the posture summary interface includes a second affordance for presenting a detailed accumulated strain value interface associated with the accumulated strain value for the user over at least the respective time window. As one example, the posture summary interface  615  in  FIG.  6 B  includes a second affordance  619  for presenting a detailed accumulated strain interface associated with the accumulated strain value for the user over at least the respective time window (e.g., shown in  FIG.  6 H ). For example, the second affordance  619  may be selected with a touch input, hand tracking input, gaze input, voice command, or the like. 
     As represented by block  710 , the method  700  includes detecting a user input, via the one or more input devices, directed to the first affordance within the posture summary interface. In some implementations, the second user input corresponds to one of a touch input directed to the display device, a voice input, a gaze input, or a hand/extremity tracking input. As one example, with reference to  FIG.  6 B , the electronic device  120  detects a second user input  622  (e.g., tap input, touch input, or the like) directed to the first affordance  612  (e.g., via the display  122 —a touchscreen). 
     As represented by block  712 , in response to detecting the user input directed to the first affordance within the posture summary interface, the method  700  includes presenting, via the display device, an animation of the representation of the user over the respective time window that corresponds to one or more instances in which head pose information changes (and/or body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than a significance threshold, wherein an appearance of the visualization of the current accumulated strain value for the user changes to represent the accumulated strain value for the user over the respective time window. In some implementations, the head pose information changes correspond to displacement, velocity, acceleration, jerk, etc. of the head pose information. When body pose information is considered, the body pose information changes correspond to displacement, velocity, acceleration, jerk, etc. of the body pose information. In some implementations, the computing systems removes the first affordance from the posture summary interface in response to detecting the user input directed to the first affordance. In some implementations, the visual animation of the one or more instances (e.g., as shown in  FIGS.  6 C- 6 F ) in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold may be correlated with audio feedback, haptic feedback, additional visual feedback (e.g., increased or decreased brightness, contrast, etc.), and/or the like to further indicate the increases or decreases to the accumulated strain value. 
     In some implementations, the significance threshold is deterministic or non-deterministic based on the number of identified instances. For example, the significance threshold changes such that only N instances are shown by the animation. In some implementations, the significance threshold is deterministic or non-deterministic based on use preferences, user history, or the like. For example, the significance threshold may be higher for a user associated with frequent large variances in the accumulated strain value, whereas the significance threshold may be lower for a user associated with smaller variances in the accumulated strain value. As such, the one or more instances are selected as “highlights” to be viewed by the user. However, the user is able to scrub to any timestamp within the respective time window. 
     As one example, with reference to the sequence in  FIGS.  6 C- 6 F , in response to detecting the second user input  622  directed to the first affordance  612  in  FIG.  6 B , the electronic device  120  presents an animation of the representation of the user  617  over the respective time window that corresponds to one or more instances in which head pose information changes (and/or body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than a significance threshold. According to some implementations, the representation of the user  617  is animated according to the head pose information changes (and/or the body pose information changes) during the one or more instances within the respective time window (e.g., the first through fourth instances in  FIGS.  6 C- 6 F ). Continuing with this example,  FIG.  6 C  shows a first instance associated with the time indicator  634 A among the one or more instances in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold. 
     Continuing with the above example,  FIG.  6 D  shows a second instance associated with the time indicator  634 B among the one or more instances in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold. Continuing with this example,  FIG.  6 E  shows a third instance associated with the time indicator  634 C among the one or more instances in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold. Continuing with this example,  FIG.  6 F  shows a fourth instance associated with the time indicator  634 D among the one or more instances in which the head pose information changes (and/or the body pose information changes) associated with the user caused an increase or a decrease to the accumulated strain value greater than the significance threshold. As such, the one or more instances are selected as “highlights” to be viewed by the user. However, the user may scrub to any time on the timeline by, for example, dragging the current time indicator or providing a time via a voice command. 
     In some implementations, the head pose information at least includes three degrees of freedom (3DOF) rotational values. For example, with reference to  FIGS.  4 A and  4 B , the computing system or a portion thereof (e.g., the head/body pose tracking engine  414 ) obtains (e.g., receives, retrieves, or detects/determines/generates) a pose characterization vector  415  based on the input data and updates the pose characterization vector  415  over time. For example, as shown in  FIG.  4 B , the pose characterization vector  415  includes ahead pose descriptor  442 A (e.g., upward, downward, neutral, etc.), translational values  442 B for the head pose, rotational values  442 C for the head pose, a body pose descriptor  444 A (e.g., standing, sitting, prone, etc.), translational values  444 B for body sections/extremities/limbs/joints, rotational values  444 C for the body sections/extremities/limbs/joints, and/or the like. 
     In some implementations, the body pose information at least includes 3DOF rotational values. For example, with reference to  FIGS.  4 A and  4 B , the computing system or a portion thereof (e.g., the head/body pose tracking engine  414 ) obtains (e.g., receives, retrieves, or detects/determines/generates) a pose characterization vector  415  based on the input data and updates the pose characterization vector  415  over time. For example, as shown in  FIG.  4 B , the pose characterization vector  415  includes ahead pose descriptor  442 A (e.g., upward, downward, neutral, etc.), translational values  442 B for the head pose, rotational values  442 C for the head pose, a body pose descriptor  444 A (e.g., standing, sitting, prone, etc.), translational values  444 B for body sections/extremities/limbs/joints, rotational values  444 C for the body sections/extremities/limbs/joints, and/or the like. 
     For example, with reference to  FIG.  4 C , the computing system or a portion thereof (e.g., the head/body/neck mechanics engine  462 ) obtains (e.g., receives, retrieves, or determines/generates) displacement, velocity, acceleration, jerk, torque, etc. values for the head/body/neck of the user  150  based on changes to the pose characterization vector  415 . With continued reference to  FIG.  4 C , the computing system or a portion thereof (e.g., the strain analyzer  464 ) determines current strain information  480  for one or more muscles or muscle groups based on: the displacement, velocity, acceleration, jerk, torque, etc. values for the head/body/neck of the user  150  from the head/body/neck mechanics engine  462 ; historical information  466 ; and the context information vector  470 . In some implementations, the strain analyzer  464  determines the current strain information  480  based on strain increase logic  465 A and/or strain decrease logic  465 B. As shown in  FIG.  4 D , the current strain information  480  includes accumulated strain information  486 : a current accumulated strain value  489 A associated with a function of one or more of the current muscle strain values  485 A,  485 B,  485 C,  485 D, and  485 E for muscles or muscle groups/regions  484 A,  484 B,  484 C,  484 D, and  484 E, respectively, of the user  150 ; a pointer to historical accumulated strain information  489 B within the historical information  466 ; and miscellaneous information  489 C associated with the accumulated strain information. 
     In some implementations, as represented by block  714 , the visualization of the current accumulated strain value for the user corresponds to a colored gradient, wherein the wavelength of the colored gradient is based on the current accumulated strain value. In some implementations, the wavelength of the colored gradient changes from green to red as the accumulated neck strain increases. In some implementations, the opposite occurs as the accumulated neck strain decreases. 
     As one example, with reference to the sequence in  FIGS.  6 C- 6 F , the appearance of the visualization of the accumulated strain  621  changes based on the current magnitude accumulated of the strain value indicator for the current time indicator. As one example, with reference to  FIG.  6 C , the visualization of the accumulated strain  621  is associated with a first appearance (e.g., white) based on the magnitude of the current accumulated strain value indicator  632 A. As another example, with reference to  FIG.  6 D , the visualization of the accumulated strain  621  is associated with a second appearance (e.g., light grey) based on the magnitude of the current accumulated strain value indicator  632 B that is greater than the magnitude of the accumulated strain value indicator  632 A in  FIG.  6 C . 
     As yet another example, with reference to  FIG.  6 E , the visualization of the accumulated strain  621  is associated with a third appearance (e.g., dark grey) based on the magnitude of the current accumulated strain value indicator  632 C that is greater than the magnitude of the accumulated strain value indicator  632 B in  FIG.  6 D . As yet another example, with reference to  FIG.  6 F , the visualization of the accumulated strain  621  is associated with a fourth appearance (e.g., black) based on the magnitude of the current accumulated strain value indicator  632 D that is greater than the magnitude of the accumulated strain value indicator  632 C in  FIG.  6 E . As such, with reference to  FIGS.  6 C- 6 F , the appearance of the visualization of the accumulated strain  621  darkens as the accumulated strain value increases, and the appearance of the visualization of the accumulated strain  621  would lighten if the accumulated strain value decreased. 
     In some implementations, as represented by block  716 , the representation of the user is animated according to the head pose information changes (and/or the body pose information changes) during the one or more instances within the respective time window. As mentioned above, in some implementations, the representation of the user  617  is animated according to the head pose information changes (and/or the body pose information changes) during the one or more instances within the respective time window (e.g., the first through fourth instances in  FIGS.  6 C- 6 F , respectively). In some implementations, as represented by block  718 , the representation of the user corresponds to an avatar with a face (and/or a body) modeled on the user of the computing system. 
     In some implementations, as represented by block  720 , while presenting the animation of the representation of the user over the respective time window, the method  700  includes presenting, via the display device, a second affordance for presenting a detailed accumulated strain value interface associated with the accumulated strain value for the user over at least the respective time window and a third affordance for initiating a stretching session, wherein an appearance of the first and second affordances change to represent the accumulated strain value for the user over the respective time window. As one example, in  FIGS.  6 C- 6 F , the posture summary interface  615  includes a second affordance  619  for presenting a detailed accumulated strain interface associated with the accumulated strain value for the user over at least the respective time window (e.g., shown in  FIG.  6 H ) and a third affordance  631  for initiating a stretching session. 
     In some implementations, an appearance of the second affordance  619  and the third affordance  631  within the posture summary interface  615  change to represent the accumulated strain value for the user over the respective time window. For example, with reference to  FIG.  6 C , the appearance of the second affordance  619  and the third affordance  631  correspond to the first appearance (e.g., white) associated with the visualization of the accumulated strain  621 . As such, the appearance of the second affordance  619  and the third affordance  631  also change based on the current magnitude accumulated of the strain value indicator for the current time indicator. 
     In some implementations, as represented by block  722 , the detailed accumulated strain value interface illustrates at least one of: a graph of the accumulated strain value over at least the respective time window or current strain values for individual muscles or muscle groups/regions that comprise the accumulated strain value. In some implementations, the detailed accumulated strain value interface illustrates a graph of the accumulated strain value over at least the respective time window. For example, the graph may include previous time windows as well to provide additional context. In some implementations, the detailed accumulated strain value interface also illustrates a current accumulated strain value and (optionally) current strain values for individual muscles or muscle groups/regions. In some implementations, the user may select a specific muscle or muscle group/region to drill down in order to show details information for the specific muscle or muscle group/region. 
     As one example, with reference to  FIG.  6 F , the electronic device  120  detects a third user input  650  (e.g., tap input, touch input, or the like) directed to the second affordance  619  (e.g., via the display  122 —a touchscreen). Continuing with the example, in response to detecting the third user input  650  directed to the second affordance  619  in  FIG.  6 F , the electronic device  120  presents a detailed accumulated strain interface  655  in  FIG.  6 G . 
     As shown in  FIG.  6 G , for example, the detailed accumulated strain interface  655  includes a graph  656  of the accumulated strain value over at least the respective time window, where the endpoint  658  corresponds to the magnitude of the current accumulated strain value indicator  624 A in  FIG.  6 B . Furthermore, as shown in  FIG.  6 G , the detailed accumulated strain interface  655  also includes an illustration of the muscles or muscle groups/regions  484 A,  484 B,  484 C,  484 D, and  484 E of the user  150  as well as current accumulated muscle strain values  485 A,  485 B,  485 C,  485 D, and  485 E for the muscles or muscle groups/regions  484 A,  484 B,  484 C,  484 D, and  484 E, respectively. In some implementations, the user may drill down into detailed muscle strain information for a specific muscle or muscle group/region as illustrated by the detailed muscle strain interface  665  in  FIG.  6 H . 
     In some implementations, the accumulated strain value is based on a plurality of strain values for a plurality of muscles or muscle groups/regions of the user. In some implementations, the accumulated strain value is a function of the plurality of strain values for the plurality of muscles or muscle groups/regions of the user. For example, some muscles or muscle groups/regions may be weighted differently based on user preferences, user history, or the like. As shown in  FIG.  4 D , the current strain information  480  includes accumulated strain information  486  with: a current accumulated strain value  489 A associated with a function of one or more of the current muscle strain values  485 A,  485 B,  485 C,  485 D, and  485 E for muscles or muscle groups/regions  484 A,  484 B,  484 C,  484 D, and  484 E, respectively, of the user  150 ; a pointer to historical accumulated strain information  489 B within the historical information  466 ; and miscellaneous information  489 C associated with the accumulated strain information. 
     In some implementations, as represented by block  724 , selection of the third affordance causes initiation of a stretching session to ameliorate the accumulated strain value. In some implementations, the third affordance initiates a stretching session. For example, the computing system may present the stretching session based on U.S. Non-Provisional patent application Ser. No. 18/200,542, filed on May 22, 2023, which is incorporated by reference in its entirety. 
     While various aspects of implementations within the scope of the appended claims are described above, it should be apparent that the various features of implementations described above may be embodied in a wide variety of forms and that any specific structure and/or function described above is merely illustrative. Based on the present disclosure one skilled in the art should appreciate that an aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method may be practiced using any number of the aspects set forth herein. In addition, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to or other than one or more of the aspects set forth herein. 
     It will also be understood that, although the terms “first”, “second”, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first media item could be termed a second media item, and, similarly, a second media item could be termed a first media item, which changing the meaning of the description, so long as the occurrences of the “first media item” are renamed consistently and the occurrences of the “second media item” are renamed consistently. The first media item and the second media item are both media items, but they are not the same media item. 
     The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting of the claims. As used in the description of the implementations and the appended claims, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting,” that a stated condition precedent is true, depending on the context. Similarly, the phrase “if it is determined [that a stated condition precedent is true]” or “if [a stated condition precedent is true]” or “when [a stated condition precedent is true]” may be construed to mean “upon determining” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context.

Metadata:
Filing Date: 20230522
Publication Date: 20240604
Grant Date: 20240604
Priority Date: 20220523
Inventors: DEMERS, MATTHEW S.
ULLAL, ADEETI V.
BRUNO, ALEXANDER G.
TRIETSCH, DANIEL M.
Negoita, Ioana
DUNNE, JAMES J.
SALTER, Thomas G.
MOORE, THOMAS J.
Assignee: APPLE INC
CPC Classifications: [{"code": "A61B5/744", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T13/80", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T13/40", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/1116", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/012", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T11/206", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T13/40", "inventive": true, "first": true, "tree": "[]"}, {"code": "A61B5/744", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/012", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T11/206", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T13/80", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2200/24", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T13/40", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T13/80", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2200/24", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T11/206", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/744", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/012", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 91325378