Computer application for determining a mobility score and providing corresponding recommendations via a computing device

Abstract

A method for facilitating a mobility assessment of a user. The method includes activating an imaging device of a computing device in response to a first user input requesting a mobility assessment. The method also includes obtaining, via the imaging device, video data of the user performing a sequence of test movements. The sequence of test movements includes a plurality of calisthenics. Further, the method includes determining, via a model of the computing device, one or more mobility metrics based on, at least, the video data. The one or more mobility metrics includes, for each test movement, at least one of a movement duration and a postural sway. Moreover, the method includes determining a mobility score for the user based, at least partially, on the one or more mobility metrics, the mobility score configured to assess mobility of the user. In addition, the method includes causing a display screen of the computing device to display the mobility score for the user.

Description

COMPUTER APPLICATION FOR DETERMINING A MOBILITY SCORE AND PROVIDING CORRESPONDING RECOMMENDATIONS VIA A COMPUTING DEVICEFIELD OF THE INVENTION

[0001] The present disclosure relates generally to a computer application implemented on a mobile computing device, a wearable computing device, and / or server system that generates a mobility score and provides recommendations to a user relating to the mobility score.BACKGROUND

[0002] Individuals are unique and their motivational and adherence patterns in striving for a behavioral goal can vary significantly. Health-related changes in response to a behavioral change can also vary between people. Advances in sensors and wearable technologies have made it increasingly possible for individuals to collect data about themselves with the goal of self-knowledge through personal data. However, gaining self- knowledge can be more challenging than only a simple task of data collection.

[0003] For example, individuals may lack a clear understanding of their mobility. A lack of a standardized way to measure mobility presents challenges for individuals to assess changes in mobility' over time. As such, individuals may lack knowledge and motivation to adopt and / or maintain behaviors that can improve their mobility.

[0004] Accordingly, the present disclosure is directed to a computer application that can be implemented on a computing device, such as a mobile computing device, to allow a user to receive a mobility score configured to assess user’s mobility'.SUMMARY OF THE INVENTION

[0005] Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or can be learned from the description, or can be learned through practice of the embodiments.

[0006] In an aspect, the present disclosure is directed to a method for facilitating a mobility assessment of a user. The method including activating an imaging device of a computing device in response to a first user input requesting a mobility assessment; obtaining, via the imaging device, video data of the user performing a sequence of test movements, the sequence of test movements comprising a plurality of calisthenics; determining, via a model of the computing device, one or more mobility metrics based on, at least, the video data, the one or more mobility metrics comprising, for each test movement, atleast one of a movement duration and a postural sway; determining a mobility score for the user based, at least partially, on the one or more mobility metrics, the mobility score configured to assess mobility of the user; and causing a display screen of the computing device to display the mobility score for the user.

[0007] In another aspect, the present disclosure is directed to a mobile computing device, including one or more processors; and one or more computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing device to perform operations. The operations including activating an imaging device of a computing device in response to a first user input requesting a mobility assessment; obtaining, via the imaging device, video data of the user performing a sequence of test movements, the sequence of test movements comprising a plurality of calisthenics; determining, via a model of the computing device, one or more mobility metrics based on, at least, the video data, the one or more mobility metrics comprising, for each test movement, at least one of a movement duration or a postural sway; determining a mobility score for the user based, at least partially, on the one or more mobility metrics, the mobility score configured to assess mobility of the user; and causing a display screen of the computing device to display the mobility score for the user.

[0008] These and other features, aspects, and advantages of various embodiments of the present disclosure will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate example embodiments of the present disclosure and, together with the description, serve to explain the related principles.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Detailed discussion of embodiments directed to one of ordinary skill in the art is set forth in the specification, which makes reference to the appended figures, in which:

[0010] FIG. 1 illustrates a schematic diagram of an example user assessment management system according to one or more embodiments of the present disclosure.

[0011] FIG. 2 illustrates an example mobile computing device according to one or more embodiments of the present disclosure.

[0012] FIG. 3 illustrates a schematic diagram of an example server system according to one or more embodiments of the present disclosure.

[0013] FIG. 4 illustrates a schematic diagram of an embodiment of a mobile computing device capturing video of a user according to embodiments of the present disclosure.

[0014] FIGS. 5A and 5B illustrate schematic diagrams of embodiments of a mobile computing device capturing video of a user performing test movements according to the present disclosure.

[0015] FIGS. 6A-6E illustrate example prompts of a mobility program implemented on a mobile computing device according to one or more embodiments of the present disclosure, particularly illustrating a mobility score that can be displayed by the mobility program.

[0016] FIG. 7 illustrates an example flow chart implemented by a mobility assessment module according to embodiments of the present disclosure.

[0017] FIG. 8 illustrates example body landmark positions according to embodiments of the present disclosure.

[0018] FIG. 9 illustrates an example of a flow diagram of a computer-implemented method according to one or more embodiments of the present disclosure.DETAILED DESCRIPTIONOverview

[0019] Repeated use of reference characters and / or numerals in the present specification and / or figures is intended to represent the same or analogous features, elements, or operations of the present disclosure. Repeated description of reference characters and / or numerals that are repeated in the present specification is omitted for brevity.

[0020] As referred to herein, the terms “includes’" and “including” are intended to be inclusive in a manner similar to the term “comprising.” As referenced herein, the terms “or” and “and / or” are generally intended to be inclusive, that is (i.e.), “A or B” or “A and / or B” are each intended to mean “A or B or both.” As referred to herein, the terms “first,” “second,” “third,” and so on. can be used interchangeably to distinguish one component or entity from another and are not intended to signify location, functionality, or importance of the individual components or entities. As referenced herein, the terms “couple,” “couples,” “coupled,” and / or “coupling” refer to chemical coupling (e.g., chemical bonding), communicative coupling, electrical and / or electromagnetic coupling (e.g., capacitive coupling, inductive coupling, direct and / or connected coupling, etc.), mechanical coupling, operative coupling, optical coupling, and / or physical coupling.

[0021] As referenced herein, the term “system” can refer to hardware (e.g., application specific hardware), computer logic that executes on a general-purpose processor (e.g., acentral processing unit (CPU)), and / or some combination thereof. In some embodiments, a “system” described herein can be implemented in hardware, application specific circuits, firmware, and / or software controlling a general-purpose processor. In some embodiments, a “system” described herein can be implemented as program code files stored on a storage device, loaded into a memory. and executed by a processor, and / or can be provided from computer program products, for example, computer-executable instructions that are stored in a tangible computer-readable storage medium (e.g.. random-access memory (RAM), hard disk, optical media, magnetic media).

[0022] As mentioned, individuals are unique and their motivational and adherence patterns to improve health-related habits vary from person-to-person. Health-related changes in response to a behavior change can also vary’ between people. Advances in sensors and wearable devices have made it increasingly possible for individuals to collect data about themselves with the goal of self-knowledge through personal data. However, gaining self- knowledge can be more challenging than only a simple task of data collection.

[0023] For example, individuals may lack a clear understanding of their mobility. A lack of a standardized way to measure mobility presents challenges for individuals to assess changes in mobility over time. As such, individuals may lack knowledge and motivation to adopt and / or maintain behaviors that can improve mobility’.

[0024] Therefore, providing context to mobility can help individuals to understand its change in response to various mobility metrics and the relationship therebetween. Moreover, understanding relationships between various mobility metrics and corresponding behaviors can provide valuable context into interpreting the mobility’ metrics and changes in mobility’ associated with a behavior.

[0025] Motivated by these gaps in understanding personal data, the present disclosure is directed to a mobility program for a computer application that is configured to assess mobility of a user according to a standardized approach. Thus, in an embodiment, the computer application of the present disclosure is configured to support individuals in understanding their mobility so as to encourage individuals to adjust their behaviors to mitigate risks of developing balance issues and / or to generally improve overall mobility. In particular, the computer application of the present disclosure enables participants to receive a mobility’ score configured to assess mobility’ of the user and track changes to their mobility’ score over time in addition to rigorous insights, contextual information, and data visualizations to help participants understand their mobility.

[0026] According to example embodiments of the present disclosure, an imaging device of a computing device (e.g., computer, a laptop, a tablet, a smartphone, a wearable computing device, etc.) can be activated in response to a first user input requesting a mobility assessment. Further, the computing device can obtain video data relating to the user performing a sequence of test movements. In an embodiment, each test movement in the sequence of test movements is configured to be performed by the user without receiving aid or resistance from an external source.

[0027] Further, the computing device can determine, via a model of the computing device, one or more mobility metrics based on, at least, the video data. The mobility' metric(s) include, for each test movement, at least one of a movement duration and a postural sway. Moreover, the computing device can determine a mobility score for the user based, at least partially, on mobility metric(s). Thus, the mobility score is configured to assess mobility of the user. In addition, the computing device can cause a display screen of the mobile device to display the mobility score for the user.

[0028] In an embodiment, the computing device described herein may further include, be coupled to, and / or otherwise be associated with one or more computing devices and / or computing systems described below and illustrated in the example embodiments depicted in FIGS. 1-10. For example, in at least one embodiment, the computing device described herein may include, be coupled to, and / or otherwise be associated with wearable computing device, mobile computing device, and / or server system.

[0029] In an embodiment, a wearable computing device, mobile computing device, and / or server system can individually and / or collectively perform the mobility monitoring and / or mobility assessment operations (e.g., determining the mobility score for the user) described herein in accordance with one or more embodiments of the present disclosure. In an embodiment, based at least in part on (e.g., in response to) performing such mobility assessment operations, the wearable computing device, mobile computing device, and / or serv er system can further perform, individually and / or collectively, one or more operations described herein that can facilitate alteration (e.g.. improvement) of a user's mobility in accordance with one or more embodiments of the present disclosure.

[0030] Further, a user may be provided with privacy-related controls allowing the user to make an election as to both if and when systems, programs, or features described herein may enable collection of health-related data and / or user information (e.g., information about a user’s social network, social actions, or activities, profession, a user’s preferences, or a user’s current location), and if the user is sent content or communications that may be of asensitive or private nature from a server. In addition, certain data may be treated in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user’s identity may be treated so that no personally identifiable information can be determined for the user, or a user’s geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined. Thus, the user may have control over what information is collected about the user, how that information is used, and what information is provided to the user. To that end, any information collected as described herein relating to the user (e.g., personal medical data, health conditions, etc.) is capable of being kept private and confidential and not being improperly used or published.

[0031] Moreover, one or more security measures can be implemented to ensure that demographic data and / or physiological data of the user is safeguarded. For example, passcode or fingerprint authentication may be used to control access to the demographic data and the physiological or otherwise personal data of the user. Further, such data of the user can be stored in a privacy enhancing manner and not shared without the express consent of the user. For example, such data can be encrypted to secure the data from unauthorized access.

[0032] Example aspects of the present disclosure provide several technical effects, benefits, and / or improvements in computing technology.Example Devices and Systems

[0033] Referring now to the drawings, FIG. 1 illustrates an example user assessment management system 100 according to one or more example embodiments of the present disclosure is illustrated. As shown, the user assessment management system 100 depicted in FIG. 1 illustrates an example networked relationship between a mobile computing device 300 and an external mobile computing device in accordance with one or more embodiments. In example embodiments, a variety of external electronic and / or computing devices can be in communication with the mobile computing device 300 to facilitate the user’s mobility assessment and / or alteration (e.g., improvement). Although the external electronic and / or computing device is depicted as a wearable computing device 200 in the embodiment illustrated in FIG. 2, it should be understood that the present disclosure is not so limiting. For instance, the external electronic and / or computing device according to example embodiments may include, for example, a personal digital assistant (PDA), a tablet, a personal computer, a laptop computer, a smart television, a video game console, and / or another electronic and / orcomputing device that can be external to the mobile computing device 300. Furthermore, while the user assessment management system 100 is described with reference to a networked relationship between two computing devices 300, 200, it should be appreciated that the present disclosure is not so limiting. For example, it should be understood that the user assessment management system 100 can be performed and / or implemented using one or more computing devices.

[0034] With reference to the example embodiment described above and depicted in FIG. 1, the mobile computing device 300 is configured to perform a mobility assessment of the user and / or perform operation(s) to facilitate alteration (e.g., improvement) of the user’s mobility based on the mobility assessment. As such, in certain embodiments, the mobile computing device 300 can be capable of and / or configured to obtain data relating to the user performing a sequence of test movements and / or perform the mobility assessment and / or operation(s) using such data. Accordingly, in an embodiment, the mobile computing device 300 may be configured to generate an intelligent notification 302 and provide the intelligent notification 302 to the user 350, e.g., via a display 314 or a second computing device. In some embodiments, the intelligent notification 302 can include the mobility assessment, which will be described further below.

[0035] However, in additional and / or alternative embodiments, the wearable computing device 200 and / or another electronic and / or computing device that can be used to obtain the data relating to the user performing the sequence of test movements can be in communication with the mobile computing device 300. In an embodiment, the wearable computing device 200 and / or another electronic and / or computing device can communicate or relay such data over one or more networks 106 to other devices. This includes, in some embodiments, relaying data to devices capable of serving as Internet-accessible data sources, thus permitting the collected data to be viewed using a web browser or network-based application at the mobile computing device 300. For example, the wearable computing device 200, e.g., while being worn by the user, can obtain, calculate, and / or store motion data relating to the user performing the sequence of test movements. The wearable computing device 200 can then transmit (e.g.. periodically or continuously) the motion data over the network(s) 106 to the mobile computing device 300 and / or a server system 112 where the data can be stored, processed, and visualized by the user and / or another entity (e.g., a health care professional). In an embodiment, the server system 112 can be configured to use the data relating to the user 350 performing the sequence of test movements to perform the mobility assessment of the user according to one or more embodiments described herein. Inan embodiment, based at least in part on (e.g., in response to) performing the mobilityassessment, the server system 112 can perform one or more operations described herein to facilitate alteration (e.g., improvement) of the user’s mobility.

[0036] Referring particularly to FIG. 2, the mobile computing device 300 according to one or more embodiments of the present disclosure is illustrated. In general, the mobile computing device 300 is described herein with reference to a smartphone. However, it should be appreciated that the mobile computing device 300 may be implemented as any suitable mobile device, including, but not limited to, a smartwatch, a laptop, a tablet, or any other computing device that is configured to perform and / or implement the mobility7assessment principles and features disclosed herein.

[0037] The mobile computing device 300 may include one or more processor(s) 310. In an embodiment, the processor(s) 310 can be configured to execute computer-readable instructions that, when executed, cause the mobile computing device 300 to perform one or more operations. In an embodiment, the processor(s) 310 can be configured to execute operational code (e.g., instructions, processing threads, software) for the mobile computing device 300 such as, for instance, firmware or the like. In the example embodiment depicted in FIG. 1, the processor(s) 310 can each be a central processing unit (CPU), microprocessor, microcontroller, integrated circuit (e.g., an application-specific integrated circuit (ASIC)), and / or another type of processing device. In an embodiment, the processor(s) 310 can be coupled to (e.g., electrically, communicatively, physically, operatively) to one or more components of the mobile computing device 300 such that the processor(s) 310 can facilitate one or more operations in accordance with the embodiments described herein.

[0038] In an embodiment, as shown in FIG. 2, the computer-readable instructions and / or operational code that can be executed by the processor(s) 310 can be stored in a memory device 312 of the mobile computing device 300. The memory device 312 can store computer-readable and / or computer executable entities (e.g., data, information, applications, models, algorithms) that can be created, modified, accessed, read, retrieved, and / or executed by each of the processor(s) 310. In some embodiments, the memory device 312 can constitute, include, be coupled to (e.g., operatively), and / or otherwise be associated with a computing system and / or media such as, for example, one or more computer-readable media, volatile memory, non-volatile memory-, random-access memory- (RAM), read only memory (ROM), hard drives, flash drives, and / or other memory devices. In these or other embodiments, such one or more computer-readable media can include, constitute, be coupledto (e.g., operatively), and / or otherwise be associated with one or more non-transitory computer-readable media.

[0039] The mobile computing device 300 may also include a display 314. The display 314 may include any type of electronic display or screen known in the art. For example, in some embodiments, the display 314 may include a liquid cry stal display (LCD) or organic light emitting diode (OLED) display such as, for instance, a transmissive LCD display or a transmissive OLED display. Further, the display 314 can be configured to provide brightness, contrast, and / or color saturation features according to display settings that can be maintained the processor(s) 310. In some embodiments, the display 314 may include a touchscreen such as, for instance, a capacitive touchscreen. For example, in these embodiments, the display 314 may’ include a surface capacitive touchscreen or a projective capacitive touch screen that can be configured to respond to contact with electrical chargeholding members or tools, such as a human finger.

[0040] In further embodiments, the mobile computing device 300 may also include at least one additional human-machine interface (HMI) 320 configured to allow the processor(s) 310 to receive conventional inputs from a user. As such, the HMI 320 can enable a user to interact with the mobile computing device 300. In some embodiments, the HMI 320 can include one or more interfaces that provide information to a user, such as a display screen a speaker, etc., and can also include one or more interfaces that allow a user to interact with information displayed on the screen, such as including a touch-screen component, a mouse component, a keyboard component, a stylus component, and the like. In some embodiments, the HMI 320 can receive information from a user. For example, the HMI 320 can receive user inputs (e.g., via sensors detecting a user pressing a virtual button on a touchscreen, via the mouse component receiving a user input specifying selection of information displayed on the screen, via the keyboard component receiving a user input specifying alphanumeric information, etc.) specifying information to the mobile computing device 300.

[0041] The mobile computing device 300 may also include one or more imaging devices 322, such as a camera (e.g., visible spectrum camera, infrared camera, hyperspectral camera, etc.). In addition, the imaging device(s) 322 may include an imaging sensor (e.g.. a complementary' metal-oxide-semiconductor (CMOS) or charge-coupled device (CCD)) to capture, detect, or recognize a user’s behavior, figure, expression, etc. The video data can include individual image frames captured by the imaging device(s) 322. The image frames can be expressed in any number of different color spaces (e.g., greyscale. RGB, CMYK. etc.).As another example, the image frames can be generated by a Light Detection and Ranging (’’LIDAR ") system or a Radio Detection and Ranging (’ RADAR ') system.

[0042] The mobile computing device 300 may also include one or more sensors 324. For example, the sensor(s) 324 may include an inertial measurement unit (IMU) 326 which includes one or more accelerometers 326a and / or one or more gyroscopes 326b. The accelerometer(s) 326a may be used to capture motion information with respect to the mobile computing device 300. The gyroscope(s) 326b may also be used additionally or alternatively to capture motion information with respect to the mobile computing device 300. For example, the IMU 326 may be configured as a six-axis or six-dimensional inertial measurement unit (e.g., a tri-axial accelerometer and a tri-axial gyroscope). The motion information obtained via the IMU 326 may be associated with the user when the mobile computing device 300 is worn or carried by the user. The IMU 326 may further include one or more magnetometer, one or more barometers, and / or any other suitable sensors.

[0043] The mobile computing device 300 may also include one or more power components 316. such as may include a battery operable to be recharged through conventional plug-in approaches, or through other approaches such as capacitive charging through proximity with a power mat or other such device.

[0044] The mobile computing device 300 may also include one or more wireless components 318 operable to allow the processor(s) 310 to communicate with one or more electronic devices within a communication range of a particular wireless channel. The wireless channel can be any appropriate channel used to enable devices to communicate wirelessly, such as Bluetooth, cellular, NFC, Ultra-Wideband (UWB), or Wi-Fi channels. It should be understood that the mobile computing device 300 can have one or more conventional wired communications connections as known in the art.

[0045] The mobile computing device 300 may also include a mobility assessment module 500 and / or other modules and / or data that can be used to facilitate one or more operations described herein. The mobility assessment module 500 may include one or more hardware and / or software components and / or features that can be configured to perform a mobility assessment of the user 350. as described further below.

[0046] Returning back to FIG. 1, in example embodiments described here, the wearable computing device 200 may be a computing device that can communicate with the mobile computing device 300 to provide data that is measured by the wearable computing device 200. The wearable computing device 200 can include some or all of the components described with respect to the mobile computing device 300, such as the display 314, thesensors 324, the processor(s) 310, etc. Therefore, descriptions of these components in the context of the mobile computing device 300 are also applicable to the wearable computing device 200 and will not be repeated for the sake of brevity. In example embodiments, the wearable computing device 200 may include a sensor, such as an IMU, disposed on, coupled to, embedded and / or integrated in, and / or otherw ise be associated with the wearable computing device 200 that can be configured to gather data regarding movements performed by a user.

[0047] Furthermore, in an embodiment, as shown in FIG. 1, the wearable computing device 200 may include a display 202, an attachment component 204, a securement component 206, and a button 208 that can be located on a side of the wearable computing device 200. In an embodiment, the display 202 can include a screen such as. for example, a touch screen that can receive inputs through (e.g., by way of) the touch of the user. In an embodiment, two sides of the display 202 can be coupled (e.g., mechanically, operatively) to the attachment component 204. In some embodiments, the securement component 206 can be located on, coupled to (e.g., mechanically, operatively), and / or integrated with the attachment component 204. In an embodiment, the securement component 206 can be positioned opposite the display 202 on an opposing end of the attachment component 204. In some embodiments, the button 208 can be located on a side of the w earable computing device 200, underneath the display 202. While the wearable computing device 200 is shown in example embodiments of the present disclosure to have the display 202, it should be understood that, in some embodiments, the wearable computing device 200 does not have any type of display unit.

[0048] The attachment component 204 can be used to attach (e.g., affix, fasten) the wearable computing device 200 to a user thereof (e.g., to the user’s 10 body or clothing). In some embodiments, the attachment component 204 can take the form of, for example, a strap, an elastic band, a rope, and / or any other form of attachment one of ordinary skill in the art would understand can be used to attach the wearable computing device 200 to a user. For example, the wearable computing device 200 can be configured as a wrist bracelet, watch, ring, electrode, finger-clip, toe-chp, chest-strap, ankle strap, and / or a device placed in a pocket. In additional or alternative embodiments, the wearable computing device 200 can be embedded in something in contact with the user 350 such as, for instance, clothing, a mat that can be positioned under the user 350, a blanket, a pillow; and / or another accessory.

[0049] The securement component 206 can facilitate attachment of the attachment component 204 upon a user of w earable computing device 200. In some embodiments, thesecurement component 206 can include, but is not limited to, a pin and hole locking mechanism (e.g.. a buckle), a magnet system, a lock, a clip, and / or any other type of securement that one of ordinary skill would understand can be used to facilitate attachment of the wearable computing device 200 to a user. In an embodiment, the wearable computing device 200 does not include the securement component 206. For example, in an embodiment, the wearable computing device 200 can be secured to a user with a strap that can be tied around the user’s wrist and / or another suitable appendage.

[0050] The button 208 can allow for a user to interact with the wearable computing device 200 and / or allow for the user to provide a form of input into wearable computing device 200. In an embodiment, the wearable computing device 200 can include a microphone that can receive inputs through (e.g., by way of) voice commands of a user.

[0051] In an embodiment, the communication between the wearable computing device 200 and the mobile computing device 300 can be facilitated by the network(s) 106. In some embodiments, the network(s) 106 may include, for instance, one or more of an ad hoc network, a peer-to-peer communication link, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the public switched telephone network (PSTN), a cellular telephone network, and / or any other type of network. In some embodiments, the communication between the wearable computing device 200 and the mobile computing device 300 can also be performed through a direct wired connection. In these or other embodiments, this direct- wired connection can be associated with any suitable or desirable communication protocol and / or physical connector such as, for instance, universal serial bus (USB), micro-USB, WiFi, Bluetooth, FireWire, PCIe, or the like.

[0052] Referring particularly to FIG. 3, the mobile computing device 300 can transmit the data relating to the user 350 performing the sequence of test movements to the server system 112 (e.g., via the network(s) 106 of FIG. 1). In this embodiment, the server system 112 can analyze the received data to perform the mobility assessment and / or can use the received data to update a user profile for the user 350 that can be stored in a database 114 (e.g., a log) of a memory 116 of the server system 1 12.

[0053] In some embodiments, the server system 112 can be implemented on one or more standalone data processing apparatuses or a distributed network of computers. In some embodiments, the server system 112 can employ various virtual devices and / or services of third-party service providers (e.g., third-party cloud service providers) to provide theunderlying computing resources and / or infrastructure resources of the server system 112. In some embodiments, the server system 112 can include, but is not limited to, a handheld computer, a tablet computer, a laptop computer, a desktop computer, or a combination of any two or more of these data processing devices or other data processing devices.

[0054] The server system 112 can include one or more processors 118 such as, for instance, one or more CPUs. In these or other embodiments, the server system 112 can include one or more network interfaces 120 that can include, for example, an input / output (I / O) interface to the mobile computing device 300 and / or the mobile computing device 300. In some embodiments, the server system 112 can include one or more communication buses for interconnecting these components.

[0055] The memory 116 according to example embodiments can include high-speed random-access memory such as, for instance, DRAM, SRAM, DDR RAM, or other randomaccess solid-state memory devices; and, optionally, can include non-volatile memory such as, for example, one or more magnetic disk storage devices, one or more optical disk storage devices, one or more flash memory devices, or one or more other non-volatile solid state storage devices. The memory 116, optionally, can include one or more storage devices that can be remotely located from the processor(s) 118 (e g., processing unit(s)). Further, the memory 116, or alternatively the non-volatile memory7within the memory7116, can include a non-transitory computer readable storage medium. In some embodiments, the memory 116, or the non-transitory computer readable storage medium of the memory7116. can store one or more programs, modules, and data structures. In these embodiments, such programs, modules, and data structures can include, but not be limited to, one or more of an operating system that can include procedures for handling various basic system sen ices and for performing hardware dependent tasks.

[0056] Referring now to FIG. 4, a schematic diagram of the mobile computing device 300 capturing data of a user 350 according to example embodiments of the present disclosure is illustrated. In an embodiment, as shown, video data may be obtained by the mobile computing device 300 via the imaging device 322 thereof. For example, the imaging device 322 of the mobile computing device 300 has a field of view 354. The mobile computing device 300 can, for example, be placed on a surface 352 such that the user 350 (e.g., the user’s body) is within the field of view 354 of the imaging device 322. The user 350 can then be instructed by the mobile computing device 300 to perform a sequence of test movements. For example, the display 314 of the mobile computing device 300 can display each test movement in the sequence of test movements for predetermined time intervals. Video data ofuser 350 can be captured and analyzed as described herein to determine a mobility score of the user 350.

[0057] In additional or alternative embodiments, motion data may be obtained by the mobile computing device 300 or the wearable computing device 200. For example, as shown, the mobile computing device 300 may be placed on the surface 352, and the wearable computing device 200 may be worn or carried by the user 350. The user 350 can then be instructed to perform the sequence of test movements, as described above. Motion data of user 350 can be captured using the wearable computing device 200 (e.g., via an IMU) and analyzed as described herein to determine a mobility7score of the user 350.

[0058] Referring now to FIGS. 5A and 5B, schematic views of the mobile computing device 300 capturing data of the user 350 performing example test movements according to embodiments of the present disclosure are illustrated. For example, video data and / or motion data can be obtained of the user 350 performing a single leg balance (e.g., with eyes open or closed), as shoyvn in FIG. 5A. As another example, video data and / or motion data can be obtained of the user performing a sit-to-stand movement, as shown in FIG. 5B. where the user 350 stands from a seated position. The test movements can include, but are not limited to, a sit-to-stand movement, single leg balance with eyes open, single leg balance with eyes closed, single leg balance with shoulder abduction, walking, running, a forward bend, a lateral bend, a stretch, a squat, a lunge, a shoulder rotation, and / or any other suitable movements to assess mobility of the user 350. The sequence of test movements may be determined empirically (e.g., based on testing data to determine an order and duration of test movements and rest periods so as to limit effects of fatigue on the user’s ability to perform each of the test movements). Moreover, the sequence of test movements includes a plurality of calisthenics (i.e., multi-joint, compound movements performed utilizing an individual’s body weight). That is, each test movement in the sequence of test movements is configured to be performed by the user 350 without receiving aid or resistance from an external source, such as exercise equipment.

[0059] Referring now to FIGS. 6A-6E, various views of a user mobility program 400 being implemented on the mobile computing device 300, the wearable computing device 200 and / or the server system 112 as described herein are illustrated. In particular, the user mobility program 400 may be implemented on a computer application programmed in any of the wearable computing device 200, the mobile computing device 300, and / or the server system 112. Accordingly, in an embodiment, the user mobility program 400 is configured to provide users with mobility assessment and monitoring. In addition, the user mobilityprogram 400 may educate users about their mobility, provide an actionable assessment about their mobility, and allow users to easily understand the magnitude and importance that adjusting lifestyle choices can have on their mobility.

[0060] In example embodiments, the user mobility program 400 may obtain demographic data that may be helpful in assessing mobility from the user 350. The demographic data may be obtained via the display 314 and / or the at least one additional HMI 320 (FIG. 2). In an embodiment, the display 314 of the mobile computing device 300 may, for example, prompt the user 350 to enter the demographic data and may include respective input boxes configured to receive user inputs specifying the corresponding demographic data. In additional or alternative embodiments, the display 202 of the wearable computing device 200 may prompt the user 350 to enter the demographic data. The demographic data may include, for example, age and / or sex of the user 350.

[0061] Referring particularly to FIG. 6 A, the user mobility program 400 is configured to activate the imaging device 322 (e.g., a camera) of the mobile computing device 300 in response to a first user input requesting a mobility assessment. For example, as shown, the display 314 of the mobile computing device 300 may display an activation screen 402 requesting the first user input to initiate the mobility’ assessment. The first user input may be detected via the display 314 and / or the at least one additional HMI 320.

[0062] Moreover, the user mobility program 400 is configured to obtain video data relating to the user 350 performing the sequence of test movements. In such embodiments, the mobile computing device 300 may be arranged such that user 350 is within the field of view 354 of the image sensor 322 w hile performing the sequence of test movements (as shown in FIGS. 4-5B). In additional or alternative embodiments, the user mobility program 400 may be further configured to provide instructions (e.g., via the display 314 and / or the at least one additional HMI 320) to the user specifying a position of the user relative to the field of view 354 of the imaging device 322. In alternative embodiments, the data may include motion data obtained using sensor 324, such as the IMU 326, of the mobile computing device 300. In such embodiments, the mobile computing device 300 may be carried by the user 350, or otherwise supported on the user’s body, while the user 350 performs the sequence of test movements. In additional or alternative embodiments, the data may include motion data obtained using the w earable computing device 200 (e.g., via an IMU). In such embodiments, the wearable computing device 200 may be worn or carried by the user 350 while the user 350 performs the sequence of test movements.

[0063] The motion data can include, but is not limited to, a type of movement (such as exercises, stretches, etc.), a duration of the movement, an intensity of the movement (e.g.. determined based on heart rate data obtained while the user 350 performs the motion), a frequency of reoccurrence of the movement (e.g., once per day, once per week, etc.), and any other suitable data associated with the movement of the user 350 while performing the movement. The user mobility program 400 may then monitor (e.g., track) the motion data for the user 350 over a period of time (e.g., 1 day. 1 week, 1 month, 3 months. 6 months, 1 year, etc.) so as to have a more accurate representation of the user’s motion data characteristics and tendencies. That is, the user mobility program 400 may maintain historical motion data for the user 350.

[0064] Referring now to FIG. 6B, the user mobility program 400 may be configured to deactivate the imaging device 322 of the mobile computing device 300 in response to a second user input permitting determination of the mobility score based on, at least, the video data. For example, the display 314 of the mobile computing device 300 may display a permission screen 404 requesting the second user input to complete the mobility assessment. The second user input may be detected via the display 314 and / or the at least one additional HMI 320. The display 314 may be configured to display the permission screen 404, for example, a predetermined time after initiating the mobility assessment.

[0065] As another example, the display 314 may be configured to display the permission screen 404 after the user is detected (e.g., using known machine vision techniques) performing a last test movement in the sequence of test movements. Displaying the permission screen 404 may permit the user to continue with the mobility assessment using the video data or to obtain updated video data (e.g., of the user reperforming the sequence of test movements) to continue with the mobility assessment. When the user provides the second input denying determination of the mobility score, the user mobility- program 400 may be configured to provide (e.g., via the display 314) a prompt for the user to reperform the sequence of test movements. In additional or alternative embodiments, the user mobilityprogram 400 may be configured to provide (e.g., via the display 314) a prompt for the user to reperform the sequence of test movements in response to detecting (e.g., using known machine vision techniques) the user performing at least some of the test movements at least partially outside the field of view 354 of the imaging device 322.

[0066] Referring now to FIG. 6C, the user mobility program 400 is configured to determine a mobility score 405 (e.g., MScore) of the user 350 and to provide the mobility score 405 to the user 350. For example, as shown, the display 314 of the mobile computingdevice 300 may display a mobility screen 406 specifying the mobility score 405. In additional and / or alternative embodiments, the display 202 of the wearable computing device 200 may display the mobility screen 406. Displaying the mobility score 405 to the user 350 allows the user to easily view and track the mobility score 405. The mobility score 405 is configured to assess mobility of the user. For example, higher values for the mobility score 405 may represent superior mobility for a particular age, and lower values for the mobility score 405 may indicate inferior mobility for the particular age.

[0067] In an embodiment, the user mobility program 400 may be configured to normalize (e.g., according to known data normalization techniques) the mobility score 405. For example, the mobility score 405 may be normalized using min-max normalization such that normalized the mobility score 405 is a value between zero (0) and one (1). inclusive.

[0068] Further, as shown, the user mobility program 400 may be configured to categorize the mobility score 405 for the user 350. In an embodiment, the user mobility program 400 can, for example, categorize the mobility score 405 based, at least in part, on the demographic data of the user 350 (e.g., according to known percentile score and / or bucketing techniques). For example, categories 407 (e.g.. MSCat) may be defined by percentile ranges of mobility score 405 for users within respective age groups (e.g., defined by successive age ranges (e.g., a 10-year range)). In certain embodiments, the categories 407 may be identified by text strings, such as “Poof, “Fair”. “Good”, “Very Good”, and “Excellent”. In alternative embodiments, the categories 407 may be identified on a scale (e.g.. of 1 to 5. with 1 being poor and 5 being excellent). As such the categories 407 may be configured to indicate mobility7of the user 350 relative to mobility of all users 10. The display 314 of the mobile computing device 300 displays the category 407 of the mobility score 405, as shown in FIG. 6C. so as to assist users 10 in understanding their mobility7relative to other users.

[0069] In addition, the mobility screen 406 may specify one or more mobility' metrics 408 associated with the mobility' score 405, as shown. For example, the mobility7metric(s) 408 displayed on the mobility7screen 406 may be used to determine the mobility7score 405, as discussed further below.

[0070] Furthermore, the user mobility program 400 may be configured to provide historical mobility scores for the user 350. For example, in response to the user 350 selecting a “MOBILITY SCORE TRENDS” button 414 on the display 314, the display 314 maydisplay mobility scores 405 for a period of time (e.g., 1 month, 6 months. 1 year, 3 years, etc.). Displaying the historical mobility scores allows the user 350 to easily understand the change in their mobility score 405 in response to changes in their activities. For example, thehistorical mobility scores may further specify a change in activity associated with the corresponding historical mobility score. As such, the user mobility program 400 may be further configured to educate users about the influence of various activities over a period of time on their mobility.

[0071] Moreover, the user mobility program 400 may be configured to determine recommendations regarding activities for the user 350. For example, in response to the user 350 selecting a "‘RECOMMENDATIONS7’ button 410 on the display 314, the display 314 may display a recommendation screen 412 including one or more recommendations, as shown in FIG. 6D. The recommendations may be manually selected by the user 350 and may include a measurable amount (e.g., how often and how much), relevancy (e.g., likelihood to improve one or more mobility metrics), and / or a defined time for a recommended activity. The recommendations may be determined based on one or more machine-learned models, a look-up table, or the like, that associates various activities with various changes in the mobility metric(s) 408.

[0072] For example, in an embodiment, the user mobility program 400 may include one or more machine-learned models that can include at least one recommendation generation model. In such embodiments, the recommendation generation model(s) is configured to generate one or more recommendations regarding activities for the user using machine learning. For example, in an embodiment, the machine-learned model(s) may be trained to output the recommendation(s) in response to receiving the mobility score 405 and the category 407 of the mobility score 405. In such embodiments, the recommendation(s) can include natural language recommendations of one or more activities that can be performed by the user to reach and / or remain within a specified category 407.

[0073] The machine-learned model(s) as described herein may include neural networks (e.g., deep neural networks) or a generative model (e.g., large language models (LLM), non-linear models or linear models, decision tree based models, support vector machines, hidden Markov models, Bayesian networks, and / or k-means clustering models, etc.). Example machine-learned models can also use other architectures in lieu of or in addition to those models specifically mentioned herein.

[0074] The recommendation can be generated based on the video data, the mobility score 405, and / or any other relevant information. The recommendation can correspond to at least one remedial action to improve the determined mobility score 405. As an example, responsive to determining a relatively low mobility score 405 for the particular user (e.g., based on the mobility score 405 corresponding to a ’‘Poor” or “Fair” category 407), therecommendation can include an indication that the user’s mobility score 405 is low and should be increased. In some implementations, the remedial action can include dietary suggestions (e.g., eat more or less particular foods, consume X calories, eat a low carb diet, etc.,), exercise suggestions (e.g., perform a particular work out, achieve a certain number of steps per day, etc.,), or any other lifesty le change suggestions (e.g., sleep for Y hours per night). For instance, a particular movement can be known (e.g., based on academic studies and / or historical data captured by the user assessment management system 100) to be particularly effective at improving the mobility score 405. As such, the recommendation can include the particular movement to target an improvement in the mobility' score 405. In this way, the effectiveness of the recommendations in improving the user’s mobility score 405 can be improved.

[0075] In some implementations, the recommendations can be tailored to the video data. For instance, it may be determined that a decrease in postural sway would make a large improvement to the user’s mobility' score 405 relative to a similar level of improvement in duration of movement (or in other words, it may be determined that the user’s mobility score 405 is more sensitive to changes in postural sway). As an example, assume that, for the user, it is determined that an improvement in duration of movement by' 10% would not result in a change in the mobility' score 405, whereas a decrease in postural sway by 10% would improve the user’s score. Responsive to this determination, the recommendation can additionally or alternatively include an indication that the user should decrease postural sway rather than improve duration of movement (or at least prioritize improvement in the postural sway over improvement in duration of movement).

[0076] Additionally or alternatively, the recommendations can be generated based on past behavior of the particular user and / or one or more other users. For instance, based on historical data associated with the particular user (and / or one or more other users), it can be determined that the particular user (and / or one or more other users) has previously been more likely to perform remedial actions of a particular ty pe (at least relative to remedial actions of another type). As an example, assume that the particular user has previously been presented with a recommendation including a first exercise and a recommendation including a second exercise. Responsive to determining that the user followed the recommendation including the second exercise and did not follow the recommendation including a first exercise, the recommendations generated subsequently can be more likely to include second exercises rather than first exercises. As mentioned, this past behavioral information can also be aggregated across one or more other users. The one or more other users can be selected froma larger group of users based on one or more atributes of the particular user (e.g., age, sex, fitness profile, etc.). In this way, the effectiveness of the recommendations in modifying the behavior of users can improve over time.

[0077] In addition, in some embodiments, the user mobility program 400 may be configured to prompt the user 350 to reperform the sequence of test movements after expiration of a timer. In such embodiments, the prompt may be manually selected by the user 350 (e.g.. via the HMI 320) when the user 350 is able to reperform the sequence of test movements. In example embodiments, the user mobility program 400 may initiate the timer in response to the user completing the sequence of test movements. As such, a duration of the timer may specify an amount of time (e.g., 1 week, 1 month, etc.) for the user 350 to wait until reperforming the sequence of test movements. The duration of the timer may be determined empirically (e.g., based on testing data to determine an average amount of time during which a user can perform sufficient activities so as to alter the user’s current mobility score).

[0078] In such embodiments, the user mobility program 400 may be configured to obtain updated data (e.g., video and / or motion data) for the user 350 while the user 350 reperforms the sequence of test movements. In some embodiments, as shown in FIG. 6E, the user mobility program 400 may be configured to determine an updated mobility score (e.g., UMScore) 409 based on the updated data for the user 350. In such embodiments, the display- 314 may display a comparison between the mobility- score 405 and the updated mobility score 409, as shown in FIG. 6E. Comparisons between the data for the user 350 and the updated data for the user 350 may also be displayed via the display 314, as shown. Displaying the comparisons to the user 350 can educate the user 350 on how performing various activities influences changes to the mobility score 405.

[0079] Referring now to FIG. 7, the mobility assessment module 500 is configured to determine the mobility score 405 of the user 350. The mobility assessment module 500 may include a body landmark model 506 that can receive video data 502 and / or motion data 504 of the user 350 performing the sequence of test movements. As discussed above, the video data 502 and / or the motion data 504 can be obtained using the mobile computing device 300 and / or the wearable computing device 200 while the user 350 performs the sequence of test movements. The body landmark model 506 may be configured to determine one or more body landmarks 508 based on the video data 502 and / or the motion data 504. In an embodiment, the body landmark model 506 can be a software program loaded in memory andexecuted by a processor included in a computing device (e.g., the wearable computing device 200, the mobile computing device 300 and / or the server system 112).

[0080] In some embodiments, the body landmark model 506 may generate a skeletal model (e.g., according to known computer vision techniques, such as Skelton-based Action Recognition). In such embodiments, the body landmark(s) 508 can include positions on body of the user 350. That is, the body landmark(s) 508 can be indicative of a skeletal model of a user’s 10 body. The skeletal model can include coordinates (e.g.. three-dimensional coordinates) of body positions (e.g., joints, bones, outlines, etc.). The skeletal model can be indicative of the user’s 10 whole body or a portion thereof (e.g., a torso, an arm, a leg, a hand, a neck, etc.).

[0081] Furthermore, in some embodiments, the body landmark(s) 508 can include a time series of coordinates on the user’s body. For example, the coordinates can describe movement of the body landmarks (e.g., in three-dimensional space) over time. That is, the body landmarks 508 can be descriptive of a body at various points in time. For example, as shown in FIG. 8, the body landmarks 508 can be descriptive of a body at a first point in time. Further, the body landmarks 518 can be descriptive of a body at a second point in time. As such, the body landmarks 508, 518 can illustrate how the body shifts over time. As an example, a landmark 509 in the body landmarks 508 can correspond to a landmark 519 in the body landmarks 518. As such, the landmarks 509. 519 can illustrate at least a portion of a time-series that can be output from the body landmark model 506 based on the video data 502 and / or the motion data 504 obtained while the user 350 performs the sequence of test movements. In some examples, swing angle and / or frequency from limb(s), joint range of motion, postural sway, and / or other biomarkers are able to be detected by the machine- learned model 510. The example body landmarks 508, 518 depicted in FIG. 8 can be useful in motion analysis of a user, such as in evaluating flexibility tests, balance tests, gait / walking tests, evaluating a user’s gestures in arising from a seat, evaluating a user’s mobility, or other full- or partial-body test of the user’s motor skills.

[0082] Returning back to FIG. 7, as shown, the mobility assessment module 500 inputs the body landmark(s) 508 into a machine-learned model 510. The mobility assessment module 500 receives the mobility metric(s) 408 as a prediction output by the machine-learned model 510. More particularly, as shown, the machine-learned model 510 can include a mobility metric model 511 trained to accept the body landmark(s) 508 as input and to generate an output of the mobility metric(s) 408 for the user 350 based on such inputs. The machine-learned model 510 may be trained with ground truth data (i.e., data about a real-world condition or state). The ground truth data may include video data and / or motion data of various users performing the sequence of test movements and annotated with the mobility metric(s) 408. Training the machine-learned model 510 can include updating weights and biases via suitable techniques, such as back-propagation.

[0083] The machine-learned model 510 can be or include one or multiple machine- learned models or model components. Example machine-learned models can include neural networks (e.g.. deep neural networks) or a generative model (e.g., large language models (LLM), non-linear models or linear models, decision tree based models, support vector machines, hidden Markov models, Bayesian networks, and / or k-means clustering models, etc.). Example machine-learned models can also use other architectures in lieu of or in addition to those models specifically mentioned herein.

[0084] Neural networks, such as those described herein, can include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, and / or other forms of neural networks. Some example machine-learned models can leverage an attention mechanism such as self-attention. For example, some example machine-learned models can include multi-headed self-attention models (e.g., transformer models). In another embodiment, the machine learning models described herein may include a rule-based approach, wherein actions are chosen based on a predetermined set of if-then rules or mathematical expressions with pre-defined parameters.

[0085] The mobility metric(s) 408 include, but are not limited to. at least one of a movement duration and a postural sway (FIG. 6C). The movement duration may, for example, specify respective total amounts of time that the user 350 maintained the corresponding test movements (e.g., how much time the user 350 held a balance movement). In additional or alternative embodiments, the movement duration may specify respective total amounts of time for the user 350 to perform the corresponding test movement (e.g., how much time it took the user 350 to complete a movement). The postural sway may, for example, specify an angle of the user’s torso relative to a user’s center of gravity while the user performs the respective test movements. The postural may be determined by comparing (e.g.. using known geometric relationships) respective positions of various body landmarks 508 to a surface on which the user 350 is performing the sequence of test movements (e.g., a floor, an exercise mat, etc.).

[0086] For example, the machine-learned model 510 may be configured to count a number of repetitions performed by the user (e.g.. by using known computer vision techniques to identify repetitive movements by the user). The given time period may specifyrespective total amounts of time that the user is instructed to perform each test movement. The given time period may be a same or different amount of time for each of the test movements in the sequence of test movements. The maximum number of repetitions may specify a total number of repetitions the user performed for the respective test movement. The number of repetitions for the given time period may then be determined by dividing the maximum number of repetitions by the given time period. In additional or alternative embodiments, the mobility metric(s) 408 may further include, for example, a number of repetitions of the test movements, a range of motion of various joints during the test movements, or any other suitable metric associated with the mobility of the user.

[0087] Referring still to FIG. 7, as shown, the mobility assessment module 500 inputs the mobility metric(s) 408 to a calculation model 514. The mobility assessment module 500 receives the mobility score 405 as output from the calculation model 514. In some embodiments, the calculation model 514 may be a linear regression model. In such embodiments, the calculation model 514 can be trained with training data, including but not limited to the training data for the machine-learned model 510, as discussed above. More particularly, the linear regression model can be trained to determine coefficients that output the mobility score 405 for the mobility metric(s) 408 input to the linear regression model. In alternative embodiments, the calculation model 514 may be another type of statistical model, such as a logistical regression model, a type of machine-learned model, as discussed above.Example Methods

[0088] FIG. 9 illustrates a flow diagram of a computer-implemented method 600 according to one or more example embodiments of the present disclosure. The computer- implemented method 600 can be implemented using, for instance, the wearable computing device 200, the mobile computing device 300, and / or the server system 112 described above with reference to the example embodiments depicted in FIGS. 1-8.

[0089] The example embodiment illustrated in FIG. 9 depicts operations performed in a particular order for purposes of illustration and discussion. Those of ordinary’ skill in the art. using the disclosures provided herein, will understand that vanous operations or steps of the computer-implemented method 600 or any of the other methods disclosed herein can be adapted, modified, rearranged, performed simultaneously, include operations not illustrated, and / or modified in various w ays without deviating from the scope of the present disclosure.

[0090] As shown at (602), the computer-implemented method 600 may include activating an imaging device 322 of a computing device (e.g., the mobile computing device300 and / or the wearable computing device 200) operatively coupled to one or more processors (e.g., the processor(s) 110) in response to a first user input requesting a mobility assessment.

[0091] As shown at (604), the computer-implemented method 600 may include obtaining, via an imaging device 322 of a computing device (e.g., the mobile computing device 300 and / or the wearable computing device 200) operatively coupled to one or more processors (e.g., the processor(s) 110), video data relating to the user performing a sequence of test movements. As mentioned, the sequence of test movements comprises a plurality of calisthenics. That is, each test movement is configured to be performed by the user without receiving aid or resistance from an external source, as mentioned above.

[0092] As shown at (606), the computer-implemented method 600 may include obtaining, via a model of the computing device (e.g., the mobile computing device 300 and / or the wearable computing device 200) operatively coupled to one or more processors (e.g., the processor(s) 110), one or more mobility metrics 408 based on the video data. As mentioned, the mobility metric(s) 408 include(s), for each test movement, at least one of a maximum number of repetitions and a number of repetitions for a given time penod.

[0093] As shown at (608), the computer-implemented method 600 may include determining, by a computing device (e.g., the mobile computing device 300 and / or the wearable computing device 200) operatively coupled to one or more processors (e.g., the processor(s) 110), a mobility score 405 for the user based, at least partially, on the mobility metric(s) 408. As mentioned, the mobility score 405 is configured to assess mobility of the user.

[0094] As shown at (610), the computer-implemented method 600 may include causing, by a computing device (e.g., the mobile computing device 300 and / or the wearable computing device 200) operatively coupled to one or more processors (e.g., the processor(s) 110), a display screen of the computing device to display the mobility score 405 for the user 350. Moreover, in embodiments, the computer-implemented method 600 may include deactivating the imaging device 322 in response to a second user input permitting determination of the mobility score 405 based on. at least, the video data. Furthermore, in embodiments, the computer-implemented method 600 may include causing the display screen 314 to display a prompt instructing the user 350 to reperform the sequence of test movements in response to at least one of expiration of a timer, detection of the user 350 performing at least some of the test movements at least partially outside a field of view 354 of the imagingdevice 322, and a second user input denying determination of the mobility score 405 based on, at least, the video data 502.Additional Disclosure

[0095] The technology discussed herein makes reference to servers, databases, software applications, and other computer-based systems, as well as actions performed by, and information sent to and from such systems. The inherent flexibi 1 ity of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. For instance, processes discussed herein can be implemented using a single device or component or multiple devices or components working in combination. Databases and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel.

[0096] While the present subject matter has been described in detail with respect to various specific example embodiments thereof, each example is provided by way of explanation, not limitation of the disclosure. Those skilled in the art. upon attaining an understanding of the foregoing, can readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the subject disclosure does not preclude inclusion of such modifications, variations and / or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art. For instance, features illustrated or described as part of an embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present disclosure covers such alterations, variations, and equivalents.

Claims

WHAT IS CLAIMED IS:

1. A method for facilitating a mobility assessment of a user, the method comprising: activating an imaging device of a computing device in response to a first user input requesting a mobility assessment; obtaining, via the imaging device, video data of the user performing a sequence of test movements, the sequence of test movements comprising a plurality of calisthenics; determining, via a model of the computing device, one or more mobility metrics based on, at least, the video data, the one or more mobility7metrics comprising, for each test movement, at least one of a movement duration and a postural sway; determining a mobility score for the user based, at least partially, on the one or more mobility metrics, the mobility score configured to assess mobility of the user; and causing a display screen of the computing device to display the mobility score for the user.

2. The method of claim 1, further comprising deactivating the imaging device in response to a second user input permitting determination of the mobility score based on, at least, the video data.

3. The method of claim 1, further comprising: determining a category for the mobility score for the user based, at least in part, on demographic data for the user, the category' configured to indicate the mobility of the user relative to other users; and causing the display screen to display the category.

4. The method of claim 3, further comprising receiving the demographic data via a user input to the computing device.

5. The method of claim 1. wherein the plurality of calisthenics comprises at least one of a sit-to-stand movement, single leg balance with eyes open, single leg balance with eyes closed, single leg balance with shoulder abduction, a forward bend, running, a stretch, a squat, a lunge, a shoulder rotation, and a lateral bend.

6. The method of claim 1, wherein the model comprises a machine-learned model.

7. The method of claim 6, further comprising: determining, via a body landmark model, one or more body landmark positions using the video data as a body landmark model input; and determining, via the machine-learned model, the one or more mobility metrics based on the one or more body landmark positions.

8. The method of claim 1, further comprising: determining a recommendation regarding an activity for the user based on the mobility score; and causing the display screen to display the recommendation.

9. The method of claim 1, further comprising causing the display screen to display a prompt instructing the user to reperform the sequence of test movements in response to at least one of expiration of a timer, detection of the user performing at least some of the test movements at least partially outside a field of view of the imaging device, and a second user input denying determination of the mobility7score based on, at least, the video data.

10. The method of claim 1. wherein the computing device comprises at least one of a smartphone, a tablet, and a computer.

11. A mobile computing device, comprising: one or more processors; and one or more computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing device to perform operations, the operations comprising: activating an imaging device of a computing device in response to a first user input requesting a mobility assessment; obtaining, via the imaging device, video data of the user performing a sequence of test movements, the sequence of test movements comprising a plurality7of calisthenics; determining, via a model of the computing device, one or more mobility metrics based on, at least, the video data, the one or more mobility metricscomprising, for each test movement, at least one of a movement duration or a postural sway: determining a mobility score for the user based, at least partially, on the one or more mobility metrics, the mobility score configured to assess mobility of the user; and causing a display screen of the computing device to display the mobility score for the user.

12. The computing device of claim 11, wherein the operations further comprise deactivating the imaging device in response to a second user input permitting determination of the mobility score based on, at least, the video data.

13. The computing device of claim 11, wherein the operations further comprise: determining a category for the mobility score for the user based, at least in part, on demographic data for the user, the category configured to indicate the mobility of the user relative to other users; and causing the display screen to display the category.

14. The computing device of claim 13, wherein the operations further comprise receiving the demographic data via a user input to the computing device.

15. The computing device of claim 11, wherein the plurality’ of calisthenics comprises at least one of a sit-to-stand movement, single leg balance with eyes open, single leg balance with eyes closed, single leg balance with shoulder abduction, a forward bend, running, a stretch, a squat, a lunge, a shoulder rotation, and a lateral bend.

16. The computing device of claim 11, wherein the model comprises a machine- learned model.

17. The computing device of claim 16, wherein the operations further comprise: determining, via a body landmark model, one or more body landmark positions using the video data as a body landmark model input; and determining, via the machine-learned model, the one or more mobility metrics based on the one or more body landmark positions.

18. The computing device of claim 11. wherein the operations further comprise: determining a recommendation regarding an activity for the user based on the mobility score; and causing the display screen to display the recommendation.

19. The computing device of claim 11, wherein the operations further comprise causing the display screen to display a prompt instructing the user to reperform the sequence of test movements in response to at least one of expiration of a timer, detection of the user performing at least some of the test movements at least partially outside a field of view of the imaging device, and a second user input denying determination of the mobility score based on, at least, the video data.

20. The computing device of claim 11, wherein the computing device comprises at least one of a smartphone, a tablet, and a computer.

Images