Method and system for training a user to perform an activity - Patents.com

Abstract

The present disclosure relates to a method and system for training a user to perform a physical activity. The method includes capturing a real-time video of a user performing an activity based on an activity option selected by the user, extracting an AI model based on the activity option, processing the real-time video of the user by the AI ​​model in real time to determine a set of user performance parameters based on the user's current activity performance, overlaying the user in the real-time video with a pose skeleton model, comparing the set of user performance parameters with a set of target activity performance parameters, generating feedback for the user based on a comparison of the set of user performance parameters with the set of target activity performance parameters, and rendering the feedback on a rendering device.

Description

Cross - reference to related applications

[0001] This application claims the benefit of priority under 35 U.S.C. § 119(e) to U.S. Provisional Application No. 63 / 074,539, filed on September 4, 2020, the entire disclosure of which is incorporated herein by reference.

Technical Field

[0002] This disclosure generally relates to Activity training, and more particularly, to methods and systems for training a user to perform Activity physical activities.

Background Art

[0003] In an era of rapid urbanization and fast - paced life, in many places, people may have difficulty setting aside daily time for physical well - being and finding an appropriate work - life balance. Further, when lockdowns are imposed during a pandemic, gyms and parking lots are typically closed to the public. In such cases, it is much more convenient for a person to train at home.

[0004] Many exercise machines and training methods are monitored and configured using adjustable parameter settings based on the capabilities, goals, and specific training methods desired by the user. However, for best results and to reduce the likelihood of muscle injury and damage, many exercises require precise Performance execution of complex movements by the user during the exercise routine, and skilled adjustment of weight or force resistance.

[0005] Disadvantages associated with conventional workout machines are that the set parameters used and the changes over time during a workout session can be different. Another disadvantage is that the exercise machine does not remember previously entered customization settings or preferences. Another problem is that workout history is not saved. Also, conventional exercise machines may include a set of pre-configured programs that may not be suitable for all users. Another problem associated with conventional exercise machines is that their pre-configured programs do not take into account other parameters such as posture, body movement, and other parts.

[0006] Furthermore, at most gyms, there is typically a set of mirrors that allow a person to view and confirm or adjust their posture and movement in order to consider proper posture and movement. However, unless a person has an expert to analyze their posture and movement, the person may operate with improper posture and movement, which can result in potential injury. Additionally, an exercise trainer cannot always be present during exercise. Also, while the trainer is conducting group exercise, the trainer cannot monitor and guide all the exercisers simultaneously. Furthermore, in most cases, the trainer cannot be used to motivate / reward the exerciser.

[0007] Furthermore, sensors are known to record various information about the human body. For example, electromyogram (EMG) electrodes can measure the electrical activity generated by a person's muscles. Similarly, there are motion sensors that record a person's movement / movements. Thus, in relation to individual training, particularly in relation to self-training or individual training or remote training, current technology does not allow a coaching / training entity to monitor an individual's physiological state during individual coaching / training and / or efficiently manage an individualized exercise regimen for an individual in a real-time manner. An individual PerformanceSince it may have personalized needs regarding upward, it is desirable for the system to automatically adjust measurement criteria and instructions by considering the physiological state.

[0008] On the other hand, monitoring and evaluating exercise / fitness, matching exercise sequences, counting sequences, and tracking the real-time progress of exercise through the presence of an instructor can be time-consuming, and the reliability of the results may be low according to the subjective evaluation criteria of the instructor. Therefore, it is beneficial to use video display, artificial intelligence (AI), and augmented reality (AR) technologies to solve such problems.

[0009] Therefore, an interactive exercise machine that uses sensors to further provide an interactive rendering device for tracking the user's posture and body movements and displaying and managing exercises is highly desirable for many users from a health and fitness perspective. In addition to fitness, the interactive rendering device is desirable in various other scenarios, such as rehabilitation, physical therapy, yoga, dance, theater, and other Activity where feedback regarding calmness and observation is important.

Summary of the Invention

[0010] In one embodiment, a method for training a user to perform a body Activity is disclosed. In one example, the method includes rendering a plurality of activity types Option to the user via a graphical user interface (GUI) of a rendering device. Each of the plurality of Activity types Option includes a plurality of Activity . The method, in response to user input, selects from a plurality of Activity options and is selected by the user ActivityFurther includes receiving options. The user input includes at least one of gestures, touches, or audio commands. The method is based on at least one camera to Activity capture the real-time video of the user performing based on the selected Activity options. Each of the at least one camera captures the real-time video of the user from a related predetermined angle. The real-time video includes the postures and movements made by the user to perform the activity. The method further includes extracting an AI model based on the Capture options selected by the user. The AI model is configured to determine the deviation of the user from a plurality of correct movements associated with the Flow options based on the Activity expert. The method further includes processing the real-time video of the user in real-time by the AI model to determine a set of user Activity parameters based on the Target Activity Performance activity of the user. The method further includes overlaying the user in the real-time video with a Activity skeleton model by the AI model. The posture skeleton model includes a plurality of key points based on the Activity . Each of the plurality of key points is overlaid on the corresponding At that time of the user in the real-time video. The method further includes comparing, by the AI model, the set of user performance parameters with the Performance set of activity performance parameters. The Performance set of performance parameters corresponds to the Posture expert. The method further includes comparing, by the AI model, the set of user performance parameters with the Activity Joint of the user in the real-time video. The method further includes comparing, by the AI model, the set of user performance parameters with the Target set of activity performance parameters. The Target Activity set of performance parameters corresponds to the Activity expert. The method further includes comparing, by the AI model, the set of user performance parameters with the Target ​Further comprising generating feedback about the user based on a comparison with a set of activity performance parameters. The feedback includes at least one of a corrective measure or a warning. The feedback includes at least one of visual feedback, auditory feedback, or tactile feedback. The method further includes rendering the feedback on a rendering device by an AI model. Rendering the feedback includes overlaying at least one of the corrective measures on a pose skeleton model overlaid on the user's real-time video. Rendering the feedback further includes Warning displaying on the GUI of the rendering device. Rendering the feedback further includes outputting auditory feedback to the user via a speaker.

[0011] In one embodiment, a rendering device for training the user to perform a body Activity is disclosed. In one example, the rendering device includes a display device. The display device includes a GUI configured to render a plurality of Activity types Option to the user. Each of the plurality of Activity types Option includes a plurality of Activity . The GUI is further configured to receive a Activity option selected by the user from a plurality of Activity options in response to a user input. The user input includes at least one of a gesture, a touch, or an audio command. The rendering device further includes at least one camera configured to Activity capture the user's real-time video based on the selected Activity option and Capture capture the user's real-time video from a related predetermined angle. Each of the at least one camera Capture captures. The real-time video shows the postures and movements made by the user to perform the activityFlow including. The rendering device further includes a processor and a memory communicatively coupled to the processor. The memory stores processor instructions, and when the processor instructions are executed by the processor, the processor is caused to extract an AI model based on the Activity options selected by the user. The AI model is Activity of the expert Target Activity Performance based on, Activity associated with the Activity options to determine the deviation of the user from a plurality of correct movements. The processor-executable instructions further cause the processor, at runtime, to process the user's At that time activity Performance based on to determine a set of user Performance parameters by causing the AI model to process the user's real-time video in real time. The processor-executable instructions further cause the processor, at runtime, to overlay the user in the real-time video with the Posture skeleton model by the AI model. The pose skeleton model includes a plurality of key points based on Activity . Each of the plurality of key points is overlaid on the corresponding Joint of the user in the real-time video. The processor-executable instructions further cause the processor, at runtime, to compare a set of user performance parameters with a Target set of activity performance parameters by the AI model. Target Activity The set of performance parameters corresponds to Activity the expert. The processor-executable instructions further cause the processor, at runtime, to compare a set of user performance parameters with TargetBased on the comparison with a set of activity performance parameters, generate feedback for the user. The feedback includes at least one of corrective measures or warnings. The feedback includes at least one of visual feedback, auditory feedback, or tactile feedback. The processor-executable instructions further cause the processor, at runtime, to render the feedback on the rendering device by the AI model. Rendering the feedback includes overlaying at least one of the corrective measures on a pose skeleton model overlaid on the user's real-time video. Rendering the feedback further includes Warning displaying it on the GUI of the rendering device. Rendering the feedback further includes outputting auditory feedback to the user via a speaker.

[0012] Of course, the above general description and the following detailed description are merely illustrative and explanatory, and do not limit the invention as claimed.

Brief Description of the Drawings

[0013] The accompanying drawings incorporated in and constituting a part of this disclosure illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles.

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[0014] Exemplary embodiments will be described with reference to the accompanying drawings. For convenience, the same reference numbers are used throughout the drawings to refer to the same or similar parts. Examples and features of the disclosed principles are described herein, but modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. The following detailed description is to be regarded as illustrative only, and it is intended that the true scope and spirit be indicated by the following claims.

[0015] Referring now to FIGS. 1A - E, an exemplary rendering device 100 for training user 102 to perform a body Activity is shown. As an example, the rendering device can include, but is not limited to, a smart television (TV), desktop, laptop, personal computer, smartphone, smart mirror, voice assistant, or any computing device. In an exemplary scenario, user 102 can use rendering device 100 within room 104. Room 104 can be, for example, part of a gymnasium, physical therapy facility, rehabilitation facility, yoga studio, dance studio, dojo, martial arts center, theater coaching center, etc. The gymnasium can include a plurality of smart mirrors configured to analyze the Activity performances of multiple users. In yet another embodiment, rendering device 100 can be used in an outdoor environment (e.g., a park). In some embodiments, rendering device 100 can be used to monitor the progress of a patient undergoing rehabilitation or physical therapy.

[0016] The rendering device 100 may include a display device 106, at least one camera 108, at least one external camera 110, one or more processors (not shown), and a storage unit (not shown) communicatively coupled to the one or more processors. The display device 106 can include a graphical user interface (GUI). Note that at least one camera 108 can be disposed at the center, along the edge, or at the bottom of the smart mirror 100. The rendering device 100 uses the real-time video Capture captured by at least one camera 108 and at least one external camera 110 to Activity train the user to perform the body

[0017] As will be described in more detail in connection with FIGS. 2-11, the rendering Device 100 renders a plurality of activity types Device to the user via a graphical user interface (GUI) of the rendering Option . Each of the plurality of Activity types Option includes a plurality of Activity . The rendering device 100 further receives, in response to a user input, the Activity option selected by the user from a plurality of Activity options. The user input includes at least one of a gesture, a touch, or an audio command. The rendering device 100 further captures, by at least one camera, the real-time video of the user Activity performing an action based on the selected Activity option. Each of the at least one camera captures the real-time video of the user from a related predetermined angle Capture . The real-time video includes the Flow postures and movements made by the user to perform the activity. The rendering device 100 further extracts an AI model based on the Activity option selected by the user. The AI model isActivity Based on that of an expert Target Activity Performance , it is configured to determine the deviation of the user from a plurality of correct movements associated with the options. The rendering device 100 is further configured to process the user's real-time video in real time by an AI model to obtain the user's Activity activity Activity and determine a set of user parameters based thereon. The rendering device 100 is further configured to overlay the user in the real-time video with a At that time skeleton model by an AI model. The pose skeleton model includes a plurality of key points based on Performance . Each of the plurality of key points is overlaid on the corresponding Performance of the user in the real-time video. The rendering device 100 is further configured to compare a set of user performance parameters with a Posture set of activity performance parameters by an AI model. Activity The set of performance parameters corresponds to an expert. The rendering device 100 is further configured to generate feedback for the user based on the comparison between the set of user performance parameters and the Joint set of activity performance parameters. The feedback includes at least one of corrective measures or warnings. The feedback includes at least one of visual feedback, auditory feedback, or tactile feedback. Further, the rendering Target device 100 renders feedback for the rendering by the AI model. Rendering the feedback includes overlaying at least one of the corrective measures on the pose skeleton model overlaid on the user's real-time video. Rendering the feedback includes presenting on the GUI of the rendering device Target Activity at least one of the corrective measures Activity The set of performance parameters corresponds to an expert. The rendering device 100 is further configured to generate feedback for the user based on the comparison between the set of user performance parameters and the Target set of activity performance parameters. The feedback includes at least one of corrective measures or warnings. The feedback includes at least one of visual feedback, auditory feedback, or tactile feedback. Further, the rendering Device device 100 renders feedback for the rendering by the AI model. Rendering the feedback includes overlaying at least one of the corrective measures on the pose skeleton model overlaid on the user's real-time video. Rendering the feedback includes presenting on the GUI of the rendering device Device at least one of the corrective measures WarningFurther includes displaying. Rendering feedback further includes outputting auditory feedback to the user via a speaker.

[0018] The memory can include an AI model. Further, the memory, when executed by one or more processors according to aspects of the present disclosure, can store instructions for training one or more processors to perform physical Activity in real time for user 102. The memory can also store various data that can be processed and / or requested by rendering device 100 (e.g., real-time video AI model data, multiple Capture s, multiple Activity Type s, real-time video, a set of user performance parameters, Activity performance data, etc.). Target Activity

[0019] Rendering device 100 can interact with user 102 via a GUI accessible via display device 106. As an example, display 106 can be a liquid crystal display (LCD), a light-emitting diode (LED) backlit LCD, a thin-film transistor (TFT) LCD, an LED display, an organic LED (OLED) display, an active matrix organic LED (AMOLED) display, a plasma display panel (PDP) display, a quantum dot LED (QLED) display, etc. Rendering device 100 can also include one or more external devices (not shown). In some embodiments, rendering device 100 can interact with one or more external devices via a communication network (e.g., a universal serial bus (USB) data cable, a high-definition multimedia interface (HDMI) cable, wireless fidelity (Wi-Fi), light fidelity (Li-Fi), Bluetooth®, and other similar communication networks) for sending or receiving various data. The external device can include, but is not limited to, a remote server, a digital device, or another computing system.

[0020] The GUI renders a plurality of Activity Type for the user 102. Each of the plurality of Activity Type includes a plurality of Activity The user 102 can select from a plurality of Activity Type and Activity Type and can select from a plurality of Activity Type associated with the user command via the user command. Further, the rendering device 100 starts an activity. The GUI Activity displays 112 corresponding to the Activity expert on the screen. Activity 112 may be Target Activity Performance the video recording of the expert, Target Activity Performance 112 is Activity the 3D model of the expert, the 2D model, or Activity the 4D of the expert. The user 102 can Activity follow the activity of At that time 112 in the activity of Performance . The display device 106 shows the real-time video of the Target performance of the user 102. Performance Further, at least one camera 108 and at least one external camera 110 capture the real-time video associated with the At that time performance of the user 102. At least one external camera 110 improves the accuracy of determining the head posture, movement, line of sight, and orientation of the user 102. In addition, at least one camera 108 can be used for face recognition of the user 102. The face data corresponding to the user 102 is associated with the user profile. The user profile is stored in a database and can be associated with current and historical user data such as, but not limited to, history, custom settings, Activity messages from experts, profile data, and other similar data.

[0021] Furthermore, the face data corresponding to the user 102 is associated with the user profile. The user profile is stored in a database and can be associated with current and historical user data such as, but not limited to, history, custom settings, At that time messages from experts, profile data, and other similar data. Activity The user profile is stored in a database and can be associated with current and historical user data such as, but not limited to, history, custom settings, Activity messages from experts, profile data, and other similar data.

[0022] Also, the rendering Device 100 extracts an AI model based on the Activity options selected by the user 102. The AI model is Activity of an expert Target Activity Performance based on 112 Activity and corresponding to the Activity options. It is configured to determine the deviation of the user 102 from a plurality of correct movements related thereto. Further, the AI model receives a real-time video, processes the real-time video, and determines a set of At that time user Performance parameters based on the Performance activity of the user. In one embodiment, the rendering device 100 is configured to automatically adjust at an angle based on the estimated future orientation of the user 102's head. For example, when the user 102 is performing At that time of Activity in a lying position, the rendering device 100 can rotate approximately 90 degrees to provide At that time of Activity instantaneous tracking of performance.

[0023] Furthermore, the AI model superimposes the user 102 in the real-time video Posture with the skeletal model 114. In some configurations, the AI model can be an AI prediction model. In one embodiment, the pose skeletal model 114 can be determined based on the estimated future pose and movement of the user 102. The pose skeletal model 114 includes a plurality of key points Activity based on the type and Activity . Each of the plurality of key points corresponds to a joint of the user 102. Additionally, the plurality of key points can be connected by lines representing the bones of the user 102 to complete the pose skeletal model 114.

[0024] The display device 106 of the rendering device 100 includes a pose skeleton model 114 overlaid on the real-time video of the user 102, and Activity the Target Activity expert's At that time performance 112, and Activity a set of user performance parameters related to the performance, and Target Activity a set of Target Activity parameters related to the performance 112. Posture Note that the skeleton model 114 is automatically adjusted and normalized with respect to the real-time video of the user 102 based on the estimated future distance of the user 102 from the rendering device 100 and the estimated future Posture and movements of the user 102. In some embodiments, Posture the transparency of the skeleton model 114 may be adjustable by the user 102. In one embodiment, the pose skeleton model 114 is completely transparent and invisible to the user 102. In such an embodiment, the pose skeleton model 114 may be used by the AI model only for calculation purposes.

[0025] The AI model compares a set of user performance parameters with Target a set of activity performance parameters. Further, the AI model generates feedback for the user 102 based on the comparison between the set of user performance parameters and Target the set of activity performance parameters. The feedback includes at least one of a corrective measure or a warning. The feedback may be at least one of visual feedback, auditory feedback, or tactile feedback. Further, the AI model renders the feedback. The rendering may include overlaying one of at least one corrective measure on the pose skeleton model 114 overlaid on the real-time video of the user 102. Further, the rendering is on the GUI of the rendering device 100 WarningIt can include displaying. Further, the rendering may include outputting auditory feedback to the user 102 via a speaker configured in the rendering device 100. The feedback is for the user 102 when the user 102 is at least partially outside the field of view of at least one camera 108, the At that time instructions for correcting the posture of the user 102, the At that time instructions for correcting the user's movement related to the posture of the user 102, and the At that time instructions for correcting the position of the user 102 may include generating a warning to the user 102.

[0026] In Figure 1A, as shown on the display device 106, the user 102 follows the target Activity Performance 112. In an exemplary scenario, the At that time of the user 102, and the related pose skeleton model 114 follow the Activity Performance expert's Activity 112. Target Activity Performance 112. Activity The expert's Target Activity Performance 112 completely overlaps with the real-time video of the user 102. In such a scenario, as long as the rendering device 100 is At that time following the Activity Performance of Target Activity Performance 112, Target Activity Performance 112 can continue to be displayed. In some embodiments, the rendering device 100 can output a motivating audio message to prompt the user 102 to maintain the Target activity Performance following 112. At that time of the Performance activity

[0027] In another exemplary scenario, as shown on the display device 106, the user 102 cannot accurately follow the Target activity Performance 112. The At that time of the user 102, and the related pose skeleton model 114 are Activity Performance the Activity expert's Target Activity PerformanceIndicates the deviation from 112. In such a scenario, the rendering device 100 can generate feedback for the user 102 to ensure that the At that time activity Performance is Target in accordance with Performance 112. At that time activity Performance When the deviation of Performance exceeds a predetermined threshold Performance and persists for a predetermined threshold time, the rendering device 100 can pause the display device Target activity Performance of the user parameters set and 112.

[0028] In Figure 1B, the user 102 follows the target Activity Performance 112 as shown on the display device 106. In one embodiment, Target activity Performance 112 is displayed in the upper left insertion frame of the display device 106. In an exemplary scenario, the real-time video 116 of the user 102's At that time of Activity performance, and the related pose skeleton model 114 follow Activity the expert's Target Activity performance 112. Activity The expert's Target Activity Performance 112 completely overlaps with the real-time video 116 of the user 102. In such a scenario, as long as the rendering device 100 At that time of Activity Performance is Target Activity Performance following 112, Target Activity Performance 112 can continue to be displayed. In some embodiments, the rendering device 100 can output a motivating audio message to prompt the user 102 to maintain Target activity Performance following 112 At that time activity Performance of.

[0029] In another exemplary scenario, as shown on display device 106 for user 102, Target activity Performance 112 cannot be accurately followed. The At that time of Activity Performance real-time video 116 of user 102, and the associated pose skeleton model 114, Activity expert's Target Activity Performance show a deviation from 112. In such a scenario, rendering device 100 may generate feedback for user 102 to ensure that the At that time activity Performance is Target activity Performance 112 is being followed. When the deviation of the At that time activity Performance exceeds a predetermined threshold Performance and persists for a predetermined threshold time, rendering device 100 may pause the display device of the user Performance set of parameters and Target activity Performance 112.

[0030] In FIG. 1C, a front view of rendering device 100 is shown on display device 106 as user 102 follows the target Activity performance 112. The At that time of Activity real-time video corresponding to the performance of user 102 may be captured in real-time by at least one camera 108. Further, the AI model of rendering device 100 may process the real-time video and generate a pose skeleton model 114. In an exemplary scenario, as long as the Capture of user 102 At that time of Activity Performance is Target Activity Performance 112 and rendering device 100 At that time of Activity Performance is Target Activity Performance following 112, Target Activity Performance rendering device 100 can continue to display 112. In some embodiments, when rendering device 100 TargetActivity Performance In accordance with 112 At that time of the activity Performance It is possible to output a motivating audio message that prompts the user 102 to maintain it.

[0031] In another exemplary scenario, as shown on the display device 106 by the user 102, Target Activity Performance 112 cannot be accurately followed. The At that time of the user 102 Activity Performance When deviates from the target Performance exceeding a predetermined Activity Performance threshold, the rendering device 100 At that time of the Activity Performance To ensure that is following the target Activity Performance 112, it is possible to generate feedback (video, graphical, auditory, or tactile) for the user 102. At that time of the activity Performance The deviation of the Performance exceeds a predetermined threshold Performance and continues for a predetermined threshold time, the rendering device 100 may pause the display device of the user Target set of parameters and Performance activity 112.

[0032] In FIG. 1D, each of the user 102 and the user 118 is trained so that the rendering device 100 executes the body simultaneously in real time Activity . Note that the rendering device 100 can analyze the Activity Performance of each of a plurality of users simultaneously. For example, the rendering device 100 can be shared by family members in a household, gym members, patients undergoing rehabilitation treatment and / or physical therapy in a hospital, etc. The At that time of the user 102 Activity performance is that of the user 118 At that time of the ActivityIt is different from performance. The rendering device 100 displays the Activity corresponding target Activity performance. Also, the rendering Device 100 can display a pose skeleton model superimposed on the real-time video of the users 102 and 118. In some embodiments, the rendering device 100 displays the target Activity performance superimposed on the real-time video of each of the users 102 and 118. In some embodiments, the rendering device 100 displays the Target Activity performance in the inserted frame on the display device 106. Alternatively, the rendering device 100 may include a plurality of displays or screens for each of a plurality of users (e.g., users 102 and 118). A single display or screen within such a rendering device 100 can be used by one user at a time.

[0033] In FIG. 1E, the rendering device 100 analyzes the Activity performance of the user 102, and the smart mirror 120 analyzes the Activity performance of the user 118. The smart mirror 120 includes a camera and a GUI. The function of the smart mirror 120 is similar to that of the rendering device 100. The rendering Device 100 displays the real-time video corresponding to the user 102, and the smart mirror 120 displays the reflection or real-time video corresponding to the user 118. The smart mirror 120 can be communicatively coupled to the rendering device 100. In one embodiment, a combination of a plurality of devices (such as the rendering device 100 and the smart mirror 120) can be used in a public environment (such as a stadium or a park) for a plurality of users.

[0034] Referring now to FIG. 2, according to some embodiments, real-time body ActivityA functional block diagram of an exemplary system 200 for training user 202 to perform is shown. System 200 includes a rendering device 204. In some embodiments, the rendering Device 204 of system 200 is similar to rendering Device 100. The rendering device 204 includes a display device 206, a camera 208, a microphone 210, a speaker 212, a processor 214, and a memory 216. The memory 216 includes a GUI module 218, an AI model 220, and a database 222. One or more wearable sensors 224 can be worn by user 202. As an example, one or more wearable sensors 224 include an electrocardiogram (ECG) sensor, an electroencephalogram (EEG) sensor, an electromyogram (EMG) sensor, a pulse oximeter, and the like. Each of the one or more wearable sensors 224 is communicatively coupled to the rendering device 204 via a communication network. Further, the rendering device 204 includes one or more built-in sensors (e.g., proximity sensors, audio sensors, light detection and ranging (LIDAR) sensors, infrared (IR) sensors, and other motion-based sensors) and can receive additional data that can be processed and analyzed for user 202.

[0035] Furthermore, the display device 206 is configured to display a real-time video of user 202. The GUI module 218 is accessible to user 202 via the display device 206. The GUI module 218 provides a plurality of Activity Type to user 202. As an example, the plurality of Activity Type include, but are not limited to, body movement, guided meditation, yoga, physical therapy, flower arrangement, origami, dance, theater, performing arts, martial arts, speech therapy, rehabilitation, drawing, painting, any form of physical therapy and rehabilitation, CrossFit, Les Mills, F45, salsa, Bikram yoga, Orange Theory, and the like. Each of the plurality of Activity Type includes a plurality of Activity . User 202 can, via a user command, the plurality of Activity Typefrom Activity Type and Activity Type a plurality of Activity from Activity and at least one of them can be selected. The user command can be an audio command (received via the microphone 210), a touch gesture, an air gesture, an eye gesture, or a signal generated by an input device (such as a mouse, touch pad, stylus, keyboard, related connected device or controller (such as a game controller), etc.). The rendering device 204 can include a plurality of displays and a plurality of cameras to process a plurality of users simultaneously.

[0036] Furthermore, the camera 208 Activity type and Activity corresponding to the user 202 At that time of Activity Performance real-time video of is captured in real time. In some embodiments, the rendering device 204 can include one or more additional cameras (such as at least one external camera 110). The real-time video received from the camera 208 is stored in the database 222. In some embodiments, the user can edit the real-time video based on one or more user commands. The user command can be at least one of a text command, an audio command, a touch command, or a display gesture. One or more user commands include at least one of setting a start point of the real-time video, setting an end point of the real-time video, removing a background from the real-time video, assigning one or more tags to the real-time video, and sharing the real-time video with a set of other users.

[0037] Furthermore, the rendering device 204 can extract the AI model 220 based on the Activity option selected by the user 202. The AI model 220 Activity expert's Target Activity Performance based on Activitycorresponding to the option Activity configured to determine a deviation of user 202 from a plurality of correct movements associated therewith. Further, the AI model 220 receives real-time video from the camera 208 via the processor 214, processes the real-time video, and At that time activity of Performance based on the user Performance determine a set of parameters.

[0038] Further, the AI model 220 overlays the user 202 in the real-time video with Posture a skeletal model (e.g., Posture skeletal model 114). Posture The skeletal model Activity type and Activity includes a plurality of key points based on. Each of the plurality of key points corresponds to a joint or feature of the user 202 in the real-time video. Additionally, the plurality of key points Posture may be connected to a line representing the bones of the user 202 to complete the skeletal model. In one embodiment, a 3D rendering of the user 202 may be generated as a pose skeletal model. Posture The skeletal model is the current of the user 202 with respect to the rendering device 204 Posture and the estimated future distance and viewing position, current Posture and the estimated future field of view, and the current of the user 202 Posture and the estimated future Posture and movement, it should be noted that it is automatically adjusted and normalized for the real-time video of the user 202. In some embodiments, Posture the transparency of the skeletal model 114 may be adjustable by the user 202. In one embodiment, the pose skeletal model is completely transparent and invisible to the user 202. In such an embodiment, the pose skeletal model may be used by the AI model 220 only for computational purposes.

[0039] The AI model 220 determines a set of user performance parameters GoalCompare with a set of activity performance parameters. Further, the feedback to the user is based on the comparison with the set of user performance parameters Goal and is generated through AI deviation processing. The feedback includes at least one of a corrective measure or a warning. The feedback may be at least one of visual feedback, auditory feedback, or tactile feedback. Further, the AI model 220 renders the feedback. Rendering may include overlaying one of at least one corrective measure on the real-time video of the user 202 on the rendering device 204. Further, rendering Posture can include displaying on the GUI of the rendering device 204 Warning . Further, rendering can include outputting auditory feedback to the user 202 via the speaker 212. In one embodiment, the speaker 212 may be a directional speaker that provides a personalized training experience for the user 202. In some embodiments, the system 200 includes a plurality of speakers (e.g., a home theater system) installed in different areas of the room. In some embodiments, the rendering device 204 is configured to output audio feedback via a Bluetooth headset or speaker. The feedback may include generating a warning to the user 202 that includes instructions to correct the posture of the user 202 when the user 202 is at least partially outside the field of view of the camera 208, instructions to correct the movement of the user related to the posture of the user 202 At that time , and instructions to correct the position of the user 202 At that time . In some embodiments, the AI model 220 is a body in real time At that time Activity ​It can include a plurality of sub-modules that function in combination to perform the above steps for training the user to perform. In one embodiment, the AI model 220 of the rendering device 204 can include a proposal engine for providing proposals of Activity As an example, the performance data can include the exercises performed, the circuits executed, the duration of the exercises, Activity performance, personal goals, user profile, age, weight, body mass index (BMI), and the like.

[0040] The rendering device 204 At that time of Activity performance while Target activity performance complies with Target activity performance it may continue to display At that time of Activity performance , and related Posture skeletal model Activity When indicating a deviation from that of an expert Target activity performance , the rendering device 204 can generate feedback for the user 202 to ensure that the At that time of Activity performance while Target activity performance complies with. At that time Activity Performance When the deviation of Performance exceeds a predetermined threshold Performance and continues for a predetermined threshold time, the rendering device 204 can pause the display device of the Goal activity Performance set of user

[0041] As can be understood, the ActivityFeedback based on Activity does not have to be limited to instructions to execute corrective actions. Feedback may also include biometric feedback or warnings, such as any irregularities or problems in one or more of the pulse rate or heart beat of user 102, the body temperature of user 102, muscle spasms, pupil dilation, and other similar health problems. In some embodiments, the feedback may be in the form of motivation or encouragement provided to user 102 while an activity is being performed or after the completion of an activity. As an example, in the formation of audio feedback, messages such as "Great job", "Great", "Great action", "Perfectly done", "Performed like a pro", "You are the best", "The best I've seen", and other similar messages may be provided to user 102. Applause, cheers, or various exclamation sounds may also be provided to user 102 as feedback. These messages may be provided in the formation of visual feedback such that the message can be displayed in text form on the GUI of rendering device 100. Additionally, or alternatively, graphic elements such as confetti crackers, flying balloons, stadium crowd sounds, or avatars of a cheerleader, instructor, celebrity (e.g., Kai Greene, Phil Health, Ronnie Coleman, Arnold, and other famous personalities) may also be displayed to user 102. In some configurations, gamification and reward mechanisms for activities performed by the user may also be used as feedback provided to the user. As a result of such feedback, user 102 may be continuously motivated and may not feel like they are doing anything in isolation. Activity in isolation.

[0042] In some configurations, user 102 may also be able to set goals related to various Activity . In such cases, the feedback can include the status regarding the percentage of goals achieved by user 102. Activity In such cases, the feedback can include the status regarding the percentage of goals achieved by user 102.

[0043] In some embodiments, to provide feedback to user 102 on their personal smartphone device, i.e., a third-party smartphone device, rendering device 100 may be configured using an open application programming interface (API), which may enable such integration seamlessly. Further, data received from third-party smart devices may also be incorporated into rendering device 100 via the open API and further provided to user 102 via rendering device 100 using visual elements (such as graphs or charts), language and audio cues, or tactile cues. The data may also correspond to warnings and alerts generated by third-party smart devices. As an example, a smartwatch configured to sense the blood pressure of user 102 can send data regarding user 102 having high blood pressure to rendering device 100. Accordingly, rendering device 100 can render the message "Your blood pressure is too high, relax and break" to user 102 verbally or visually. Accordingly, rendering device 100 can act as a feedback collator and single-point smart device for viewing all feedback. In other words, since smart mirror 100 generates feedback on its own and also receives feedback from other smart devices, rendering device 100 assimilates all feedback, refines it, and presents it to user 102 via rendering device 100. Accordingly, the user does not need to rely on multiple devices to receive various types of feedback.

[0044] Further, user 102 can also on various social networks or with other remote users who can also use smart mirror 100 ActivityOne can desire to share performance with friends. For this purpose, the rendering device 100 can be configured using various integrations with social media applications. Examples of these social media applications can include, but are not limited to, FACEBOOK (registered trademark), WHATSAPP (registered trademark), YOUTUBE (registered trademark), and / or INSTAGRAM (registered trademark). In some embodiments, the smart mirror 100 can have these social media applications already installed. Also, the rendering Device 100 can have a social media application that is specific to it and is configured to connect only users of other renderings Device 100 and / or the smart mirror.

[0045] Thus, through integration with these social media applications, user performance can be posted and made public on one or more of these social media platforms and made available as online content for other users to access. The aforementioned reward mechanism can also be used on the social media platform. In some configurations, scores related to the user Activity can be presented on a leaderboard as points for various users who use the smart mirror 100 and / or the display device 200. Badges can also be assigned to various users based on the level of activity they perform and can be displayed on the social media platform. In addition, records related to the exercises performed can also be displayed. Furthermore, Activity the goals set by various users and the respective percentage completion of the goals can also be displayed on the social media platform. As can be understood, the feedback provided to the user can also be shared within a group of users on social media, including friends, social circles, and classes that are connected in real time.

[0046] In some embodiments, the rendering device 204 can generate voice messages (number of reps in voice form, auditory feedback to user 202, new achievements, personal best, messages from other users, advertisements, tasks, errors, warnings, etc.) for the user 202 in voice form via the speaker 212. Note that the timing and duration of the audio output can be important when generating an audio message. For example, when the user 202 is moving at high speed, some of the audio messages may become stale before they are generated. Further, some of the audio messages may become repetitive and unnatural. Further, some of the audio messages may be of higher priority (e.g., warnings and errors). In such scenarios, the audio messages can be generated via a mechanism based on a priority queue. In one embodiment, an AI-based approach can be used to generate more natural conversations for the audio messages. By AI Posture and exercise matching may be executed on a remote server, and note that keypoint recognition may be executed on an edge node. Thus, heavy video data transfer to the server can be avoided. In addition, Posture and since the exercise matching is unknown on the end-edge device, the overall security can be enhanced.

[0047] Note that all such modules 206-224 can be represented as a single module or a combination of different modules. Further, as will be understood by those skilled in the art, each of the modules 206-224 can reside, in whole or in part, on one device or multiple devices that communicate with each other. In some embodiments, each of the modules 206-224 can be implemented as a dedicated hardware circuit with a custom application specific integrated circuit (ASIC) or gate array, an off-the-shelf semiconductor such as a logic chip, a transistor, or other individual components. Each of the modules 206-224 can also be implemented in a programmable hardware device such as a field programmable gate array (FPGA), programmable array logic, programmable logic device. Alternatively, each of the modules 206-224 can be implemented in software for execution by various types of processors (e.g., processor 214). The identified modules of executable code can include, for example, one or more physical or logical blocks of computer instructions, which can be organized as, for example, objects, procedures, functions, or other constructs. Nevertheless, the executable bodies of the identified modules or components need not be physically located together, but can include different instructions stored in different locations that, when logically combined together, include the modules and achieve the specified purpose of the modules. In fact, the modules of executable code can be a single instruction, or many instructions, and can even be distributed across several different code segments, among different applications, and across the Internet, cloud, and several memory devices in parallel.

[0048] As will be understood by those skilled in the art, the body Activity To train the user to perform, various processes can be used. For example, the exemplary system 200 and associated rendering device 204 can be used to perform the body ActivityThe user can be trained to execute in real time. In particular, as will be understood by those skilled in the art, the control logic and / or automated routines for executing the techniques and processes described herein can be implemented by any of hardware, software (such as neural networks or other computational models), or a combination of hardware and software, by the system 200 and the associated rendering device 204. For example, suitable code can be accessed and executed by one or more processors on the rendering device 204 to execute some or all of the techniques described herein. Similarly, an application-specific integrated circuit (ASIC) configured to execute some or all of the processes described herein can be included in one or more processors on the rendering device 204.

[0049] Referring now to FIGS. 3A and 3B, an exemplary process 300 for training a user to execute the body Activity is shown via a flowchart. In one embodiment, the process 300 can be implemented by a rendering device 100 within the room 104. The process 300, in step 302, includes rendering a plurality of Activity types Option to the user via a graphical user interface (GUI) of the rendering device. Each of the plurality of Activity types Option includes a plurality of Activity and, in one embodiment, the GUI can be rendered on the display device 206 via the GUI module 218.

[0050] Further, the process 300, in step 304, in response to user input, a plurality of Activity selected by the user from a plurality of options Activityincluding receiving options. User input includes at least one of gestures, touches, audio commands, or signals generated by an input device (such as a keyboard, mouse, stylus, graphic pen, etc.). In one embodiment, an audio command can be received by the microphone 210.

[0051] Furthermore, in step 306, process 300 includes capturing a real-time video of the user performing the selected Activity based on the option Activity by at least one camera (for example, at least one camera 108). Each of the at least one camera captures the user's real-time video from a related predetermined angle Capture of. The real-time video includes the postures and movements Flow performed by the user to execute the activity. In some embodiments, the user can edit the real-time video based on one or more user commands. The user commands consist of at least one of text commands, voice commands, touch commands, or display gestures. As an example, one or more user commands include setting a start point of the real-time video, setting an end point of the real-time video, removing a background from the real-time video, assigning one or more tags to the real-time video, and Activity sharing the real-time video with a set of experts or other users, including at least one of these. Alternatively, Activity the expert can record and edit the real-time video corresponding to Target activity performance to be shared with the user.

[0052] Furthermore, in step 308, process 300 includes extracting an AI model (such as AI model 220) based on the option Activity selected by the user. The AI model is Activity based on the Target activity performance of the expert, Activity corresponding to the optionActivity configured to determine a user's deviation from a plurality of correct movements associated therewith. Further, in step 310, process 300 At that time activity Performance of the user Performance includes processing the user's real-time video in real time by an AI model to determine a set of user

[0053] parameters. Further, in step 312, process 300 includes overlaying, by the AI model, the user in the real-time video Posture with a skeletal model (e.g., Posture skeletal model 114). The pose skeletal model includes a plurality of key points based on Activity . Each of the plurality of key points is overlaid on the corresponding Joint of the user in the real-time video. In one embodiment, AI model 220 is configured to generate a pose skeletal model based on a real-time video corresponding to the At that time performance Activity of user 202. In such an embodiment, AI model 220 is configured to identify various joints or features of user 202 in real time and assign key points to each of the joints or features. In some embodiments, AI model 220 is configured to estimate a future distance of user 202 relative to rendering device 204, as well as a future pose and movement of the user. Further, step 312 of process 300 includes, in step 314, automatically adjusting and normalizing the pose skeletal model based on the At that time pose and estimated future distance of the user relative to the rendering device, and the At that time pose and estimated future pose and movement of the user.

[0054] Further, in step 316, process 300 includes comparing, by the AI model, a set of user performance parameters Goal with a set of activity performance parameters. Target activityThe set of performance parameters corresponds to an expert. As an example, the set of user Activity parameters includes, but is not limited to, the speed of the user Performance , the number of repetitions completed, the overall completion of the active circuit, third-party smart device information, the user's pulse rate, the user's blood pressure, and the user's movement. As an example, the set of target At that time parameters includes, but is not limited to, the target Activity performance speed of the user, the target number of repetitions, the user's target pulse rate, and the user's target movement. Activity performance The set of target parameters is the target Activity performance speed, the target number of repetitions, the user's target pulse rate, and the user's target movement, but is not limited thereto.

[0055] Furthermore, in step 318, process 300 includes generating feedback for the user based on a comparison of the set of user performance parameters with Goal the set of activity performance parameters by the AI model. The feedback includes at least one of a corrective action or a warning. The feedback includes at least one of visual feedback, auditory feedback, or tactile feedback. The feedback may include generating a warning to the user, including an instruction to correct the user's At that time posture, an instruction to correct the user's movement associated with the user's At that time posture, and an instruction to correct the user's At that time position when the user is at least partially outside the field of view of at least one camera.

[0056] Furthermore, in step 320, process 300 includes rendering feedback to the rendering device by the AI model. Further, step 320 of process 300 includes, in step 322, overlaying at least one of the corrective measures on the pose skeleton model overlaid on the user's real-time video. Further, step 320 of process 300 includes, in step 324, displaying a warning on the GUI of the rendering device. Further, step 320 of process 300 includes, in step 326, outputting auditory feedback to the user via the speaker.

[0057] In some embodiments, when process 300 At that time of Activity performance exceeds a predefined threshold during a predefined threshold time based on the comparison, a series of user Performance parameters and the target Activity performance change, including pausing the display device of the user. Further, in such embodiments, process 300 includes generating feedback about the user by the AI model based on a comparison between a set of user performance parameters and Performance a set of activity performance parameters. Note that the predefined threshold Activity performance is correlated with the predefined threshold time. For example, even if the user's alignment from the activity performance is off during a short time interval, the smart mirror 100 may pause the display device. In some embodiments, the AI model dynamically determines the values for each of the predefined threshold Goal and the predefined threshold time based on the user's skill level. In some embodiments, the AI model can use Performance various parameters other than and time. Goal alignment from the activity performance. In some embodiments, the AI model dynamically determines the values for each of the predefined threshold Performance and the predefined threshold time based on the user's skill level. In some embodiments, the AI model can use Performance various parameters other than and time.

[0058] Furthermore, in step 328, process 300, through the GUI of the rendering device, the userPerformance A set of parameters, Target activity performance A set of parameters, and Activity Of an expert Target activity performance Including displaying. In one embodiment, real-time video is of the user's At that time Of Activity performance In response to Target activity performance Corresponding to Activity Can be received in real time from an expert. In such an embodiment, the user Performance A set of parameters and Activity The goals of the expert Activity performance Are displayed in real time via the GUI via the rendering device. Goal Activity Performance Is the user's At that time Of the activity Performance Is overlaid on the real-time video. Further, process 300, in step 330, overlays in real time on the user's At that time Activity performance in the real-time video Goal Activity performance.

[0059] Next, referring to FIG. 4, an exemplary process 400 for correcting the initial position of a user, according to some embodiments, is shown via a flowchart. In one embodiment, process 400 is implemented by a rendering device 100. Process 400 includes, in step 402, detecting the initial position of the user via at least one camera (e.g., at least one camera 108). Further, process 400 includes, in step 404, determining whether the detected initial position of the user matches the initial position mapped to at least one Activity . Further, process 400 includes, in step 406, instructing the user to correct the initial position if the detected initial position does not match the initial position. Note that steps 402-406 can be repeatedly executed throughout the user's At that time Activity performance.

[0060] Referring now to FIG. 5, an exemplary process 500 for comparing a trainer avatar corresponding to an Goal activity Performance and a user avatar corresponding to the user's At that time activity Performance is shown via a flowchart. In one embodiment, process 500 is implemented by a rendering device 100. Process 500 includes, at step 502, obtaining real-time video through at least one camera Capture and then Activity rendering at least one of a trainer avatar corresponding to an Activity expert's target At that time performance Activity and a user avatar corresponding to the user's Activity performance. The trainer avatar is an

[0061] expert's 3D model, and the user avatar is a multi-dimensional model of the user. Activity Further, process 500 includes, at step 504, comparing at least one of the trainer avatar and the user avatar corresponding to the Activity target Activity performance At that time with the user's Activity performance based on Performance type and Activity performance Further, process 500 includes, at step 506, displaying a set of user At that time parameters, at least one of the trainer avatar and the user avatar corresponding to the Activity performance target At that time activity Performance in real time, and Goal activity Performance overlaying at least one of the trainer avatar and the user avatar corresponding to theAt that time Note that it can be repeatedly executed throughout the activity performance of

[0062] Referring now to FIG. 6, an exemplary GUI 600 is shown that displays a plurality of exercises 602 according to some embodiments. According to one embodiment, the plurality of exercises 602 can include, but are not limited to, side squats, lunges, squats, burpees, push - ups, anterior triceps overhead, push - ups front, dumbbell squat presses, squats front, and lunges front. Each of the plurality of exercises 602 can be filtered based on, but not limited to, a selection from a plurality of Activity type 604. As an example, the plurality of Activity type 604 can include, but are not limited to, "arm", "chest", "range", "leg", "quad", "shoulder", "squat", and "triceps". The plurality of exercises 602 can be sorted based on one of the sorting criteria 606. As an example, the sorting criteria 606 can include, but are not limited to, new exercises, most recently executed exercises, most frequently executed exercises, and exercise duration. Note that the GUI 600 is not limited to fitness and can be customized based on user requirements and use cases. For example, the GUI 600 can include Activity type for specific treatments in the case of rehabilitation, meditation for yoga, physical therapy recommended by medical professionals, etc. Activity can be included.

[0063] Referring now to FIG. 7, an exemplary GUI 700 is shown that displays a home page of a fitness application according to some embodiments. The GUI 700 can include a menu 702, customization 704, and multiple languages 706. As an example, the menu 702 can include, but is not limited to, options for the user such as "Exercises", "Circuits", "Dashboards", "Goals", "Connections", and "Calendars". Further, the customization 704 can provide the user with options for selecting a theme color. As an example, the theme colors can be, but are not limited to, blue steel plate, dark steel plate, carbon, charcoal, pastel lady, pastel girl, zeon, and zanado. As an example, the multiple languages 706 can include, but are not limited to, English, Japanese, or Hindi.

[0064] Next, referring to FIG. 8, an exemplary GUI 800 is shown that displays exercise parameters 802 according to some embodiments. When receiving a user selection for an exercise, the GUI 800 can request the exercise parameters 802 from the user. As an example, the exercise parameters 802 can include, but are not limited to, the number of reps, the number of sets, the interval, and the level of the exercise (e.g., beginner or advanced). In one embodiment, a predefined parameter group (such as CrossFit) can be provided to the user.

[0065] Referring now to FIG. 9, an exemplary GUI 900 is shown that displays a user's At that time of Activity performance 902 and a postural skeletal model 904 according to some embodiments. In one embodiment, the GUI 900 can display the user's postural skeletal model 904 overlaid on the user's real-time video by a rendering device (such as rendering device 100). Further, the GUI 900 is Activity corresponding to an expert Target activity performanceDisplay 906 in the insertion frame at the lower right of the display device. Further, the GUI 900 displays a message 908 for the user to prepare for the exercise. The message 908 may also be provided as an audio output. Further, the GUI 900 displays a set of user performance parameters, such as a rep / step counter 910, a number of reps 912, an exercise 914, a heart rate 916, and calories 918, not limited to these. The rep / step counter 910 is the sequence that the user should follow Goal in the activity performance Target posture Note that it is.

[0066] Referring now to FIG. 10, an exemplary GUI 1000 that displays a user's At that time of Activity performance 1002 and a pose skeleton model 1004 is shown. When the user checks the message 908 and enters the initial Posture for the exercise, the AI model of the smart mirror (e.g., the rendering device 100) can determine whether the initial Posture is correct. Further, the GUI 1000 displays a message 1008 that indicates to the user that the exercise has been successfully recognized. The message 1008 may also be provided as an audio output. The user can be notified via text, graphics, visual, haptic, or audio output to start the exercise. In an exemplary scenario, when the user's initial pose is incorrect, Posture a deviation from the activity performance is presented to the user via a message (text, graphics, audio, visual, or haptic). Goal a deviation from the activity performance is presented to the user via a message (text, graphics, audio, visual, or haptic).

[0067] Referring now to FIG. 11, an exemplary GUI 1100 that displays a user's At that time of Activity performance 1102 is shown. When the user starts executing the exercise, the user's At that time activityPerformance The real-time video associated with 1102 is analyzed by an AI model of a smart mirror (such as rendering device 100). At that time of Activity performance The real-time video of 1102 is Activity compared to that of an expert Target activity performance 1104. As an example, GUI 1100 can display a rep / step counter, percentage of completed reps, percentage of completed exercise, user's heart rate, calories burned by the user, or any other user performance parameter. When the user moves successfully from an initial Posture to a subsequent Posture , the step counter changes its value from "1" to "2", indicating that the user is in the second step. Note that many Activity have multiple steps. For example, a burpee rep has eight steps. At that time of Activity performance If 1102 deviates from the target 1104 by more than a predetermined threshold ability, a message 1106 is displayed on the display for the user along with corrective measures (e.g., "Straighten your back"). Message 1106 may also be provided as an audio output and a graphic display. Further Activity performance the expert's Activity 1104 is displayed for the user to follow Target activity performance of 1102. At that time of Activity performance 1102.

[0068] Some embodiments of the present disclosure can be used in a gym, rehabilitation, physical therapy in a hospital, dance studio, theater, or any other use case scenario. A gym can include, for example, a plurality of exercise machines and equipment for a user to perform a plurality of Activity exercises. The user can use a rendering device (e.g., rendering device 100) or any other display device (e.g., a smart mirror) to Activity select from a category Activity exercises Activityassociated with Activity attributes can be correspondingly selected. Multiple cameras can Activity the user Capture and can provide relevant instructions and feedback to the user for improving the Activity being executed. The camera can also be used for face recognition of the user to identify the user and provide the user with history, customized settings, messages from the trainer, profile data, and other similar data. In a gymnasium, a single rendering device can include multiple screens and GUIs to provide exercise training to multiple users. Further, the rendering device 100 is configured to output audio feedback via a Bluetooth headset or speaker.

[0069] The camera can be used to track and record the Activity of the user in the gymnasium when the user moves from one area or one machine to another machine to perform various Activity . The rendering device 100 can track the At that time progress of the user using the camera as the user moves from one area of the gymnasium to another area. The camera can enable the continuity of the user's context and information over monitoring. A gymnasium (or any other use case scenario) can have various types of Activity experts such as personal coaches, trainers, sports Activity experts, physical therapists, occupational therapists, physical education teachers, martial arts teachers, dance and choreography, sports personalities, team coaches, and demonstrators and other trainers in health and fitness.

[0070] The Activity performed in the gymnasium and the goals achieved by the user can be shared with one or more other users practicing in the gymnasium or with one or more remote users, Activity by an expert or the user.

[0071] In addition, user performance may be posted and made public on a social media platform and made available as online content for access by one or more remote users. This can be done through gamification of the activities performed by the user and by using a reward mechanism. The user Activity -related scores can be presented on a leaderboard as points. Badges can be assigned to the user based on the level of activity performed. In addition, records related to the activities performed can include, for example, the accuracy, total number, and breaks between exercises of the exercises performed, and can be provided to one or more users and rendered Device and displayed on a smart mirror. Further, the rendering device 100 can include additional features such as an unlocking feature, additional activities, designs, and other similar features.

[0072] In one embodiment, the rendering device 100 can be used to create content media, for example, to share content media including information related to the user's At that time health state, exercise routine, exercise ability, and previous records and earned rewards for the user on the social media platform. This can be done through an application programming interface (API) integrated with the rendering device 100.

[0073] The rendering device 100 can be used as a recording tool for creating new fitness content and as relevant instructions for activities to be performed by the user received via voice-based input. Further, both the display device and the smart mirror as used in a gym (or any other use case scenario) can be used to edit and review new content related to what the user has selected Activity and can be used to edit and review new content related to what the user has selected, ActivityIt can be used by an expert to review a user's session. Further, rendering Device 100 can be connected to a health and fitness application where a user can log in to rendering Device 100. Feedback received on the health and fitness application can be shared on a social platform for social engagement and can provide related data to other socially connected parties or groups in the formation of a leaderboard. Activity

[0074] Further, the rendering device 100 can be used as a recording device by the user or Activity an expert. As can be understood, the user and Activity the expert using the rendering device 100 can perform cropping, highlighting, adding audio, and audio text feedback on the smart mirror. Further, the user and Activity the expert can be permitted to add or remove background images as used in the smart mirror. Further, Activity the expert can create metadata, instructions, threshold parameters, and combinations thereof. The recorded video can be shared with other users. Further, the parameters collected by the rendering device can be processed to create metadata, instructions, threshold parameters, and combinations thereof.

[0075] The rendering device 100 can use one or more cameras and one or more other sensors to determine the position of the user during the execution of an activity. Feedback based on the activity being performed by the user is Capture Goal ​​It is not limited to instructions related to slowing down display and other media components such as video of motion, and can also include other feedback such as pulsation and rhythm audio cues such as a metronome. To generate accurate and timely feedback, Performance guidance cues, Goal movements, media and audio and audio feedback can be tightly coupled to the user's movements. The rendering device 100 can map and synchronize the media and information provided with the user's actual movements, and thus can provide corresponding relevant feedback.

[0076] In one embodiment, a multi-language voice-based interface can be provided to enable the user to navigate, select, schedule, and arrange from multiple Activity categories Activity . Voice-based input can create and save playlists, add metadata to playlists, add comments to playlists and activities using speech-to-text mechanisms and voice feedback, record new activity categories, edit and clip activities being executed, tag exercises with hashtags, for example, tag Type exercises, muscle groups or difficulty levels, replace exercise clips with alternative versions, share playlists and exercises with other users, and can be used to indicate a message for another user when sharing a playlist.

[0077] Some embodiments of the present disclosure may be implemented as an AI-based health and fitness system training method. The method includes detecting a user, determining the user's posture and body movement using a camera, further sensing the body or the user's movement, movement, position, and / or movement using a sensor, directing and monitoring exercise through the user's posture determination and body movement on a smart mirror, overlaying the posture and movement across the user's mirror reflection, providing real-time feedback, Activity overlaying the posture on an expert's video stream, indicating the posture position in conjunction with the training video and the user, tracking the movement in real time, automatically recalculating, and Goal including correlations and accuracies in the movement sequence and real-time social media sharing to groups, friends, and other people. In an alternative embodiment, an AI-based health and fitness training method includes a camera for determining the user's posture and body movement, a microphone for listening to the user's voice commands, and a speaker for providing feedback to the user regarding the user's movement, whereby the method provides for determining the user's posture and body movement in an exercise sequence for real-time tracking of the exercise for automatic recalculation, for automatic recalculation.

[0078] In one embodiment, real-time live feedback may be provided using voice control commands, visual, script, exercise sequences, and / or Activity graphic elements based on commands for exercise, video or text or graphic elements during the performance of the user. The rendering device 100 may include, for example, details related to the user's account access, the user's workout history, and other information related to the user. The rendering device 100 determines the user's Posture and body movement, overlays the user's Posture and movement on the user's mirror reflection, ActivityOverlaid on the expert's video stream Posture and can be associated with the training video and the user to Posture indicate the position. Based on the AI model, the rendering device 100 can provide live feedback to the user via, for example, audio feedback / video feedback / text feedback / graphic feedback, based on instructions related to the exercise / activity, script, sequence of exercises and / or the user's performance during the exercise.

[0079] In one embodiment, for efficient display of the guidance steps and placement of visual information related to the guidance steps, it can be presented on the screen of the rendering device. The rendering device GUI may be adjusted based on the position of the user's eyes, such that the information is appropriately placed for reflection rather than in the user's video stream Capture or placed in some fixed position.

[0080] Furthermore, a 3D model of the user's pose and movement on the smart mirror is provided. This involves overlaying the provided 3D model on the reflection, and then the 3D model can be rendered along with analysis for display via the smart mirror. As can be understood, one or more cameras that capture the user's pose and movement along the long side of the mirror can be adjusted to enable a better aspect ratio of the user's pose. Capture

[0081] Video recording can be made using at least one camera placed at a distributed location. The rendering device 100 may include a recording device for creating new training content, for recording content to be reviewed later by an expert, a physiotherapist, a teacher, a choreographer, and / or for real-time sharing of the live stream of the expert or the user. Activity Activity

[0082] ​​​ Also, as will be appreciated, the techniques described above can take the form of processes and apparatuses for performing those processes, implemented by a computer or a controller. The present disclosure can also be embodied in the form of computer program code comprising instructions embodied in a tangible medium such as a floppy disk drive (registered trademark) diskette, a solid state drive, a CD-ROM, a hard drive, or any other computer-readable storage medium, the computer program code, when loaded and executed by a computer or a controller, causing the computer to become an apparatus for practicing the invention. The present disclosure can also be embodied in the form of computer program code or a signal, whether stored in a memory medium, loaded and / or executed by a computer or a controller, or transmitted via some transmission medium such as via electrical wiring or a cable, via an optical fiber, or via electromagnetic radiation, etc., the computer program code, when loaded and executed by a computer, causing the computer to become an apparatus for practicing the invention. When implemented on a general-purpose microprocessor, segments of the computer program code configure the microprocessor to create specific logic circuits.

[0083] The disclosed methods and systems can be implemented on conventional or general-purpose computer systems such as personal computers (PCs) or server computers. Referring now to FIG. 12, an exemplary computing system 1200 is shown that can be employed to implement processing functionality for various embodiments (such as as a SIMD device, client device, server device, one or more processors, etc.). Those skilled in the art will recognize that other computer systems or architectures can also be used to implement the present invention. Computing system 1200 can represent, for example, a user device such as a desktop, laptop, mobile phone, personal entertainment device, DVR, or any other type of special or general-purpose computing device that may be desirable or appropriate for a given application or environment. Computing system 1200 can include one or more processors, such as processor 1202, which can be implemented using a special-purpose processing engine such as a microprocessor, microcontroller, or other control logic. In this example, processor 1202 is connected to bus 1204 or other communication medium. In some embodiments, processor 1202 can be an artificial intelligence (AI) processor implemented as a tensor processing unit (TPU), or a graphical processing unit, or a custom programmable solution field programmable gate array (FPGA).

[0084] Computing system 1200 can also include a memory 1206 (main memory), such as random access memory (RAM) or other dynamic memory, for storing information and instructions to be executed by processor 1202. Memory 1206 can also be used to store temporary variables or other intermediate information during execution of instructions by processor 1202. Computing system 1200 can similarly include a read-only memory (“ROM”) or other static storage device coupled to bus 1204 for storing static information and instructions for processor 1202. Computing system 1200 can also include, for example, a media drive 1210 and a storage device 1208 that can include a removable storage interface. The media drive 1210 can include a drive or other mechanism for supporting fixed or removable storage media, such as a hard disk drive, a floppy (registered trademark) disk drive, a magnetic tape drive, an SD card port, a USB port, a micro USB, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drives. The storage medium 1212 can include, for example, a hard disk, a magnetic tape, a flash drive, or other fixed or removable media that can be read and written by the media drive 1210. As these examples show, the storage medium 1212 can include a computer-readable storage medium storing specific computer software or data.

[0085] In an alternative embodiment, the storage device 1208 can include other similar means for enabling a computer program or other instructions or data to be loaded into the computing system 1200. Such means can include, for example, a removable storage unit 1214, a storage unit interface 1216 such as a program cartridge and a cartridge interface, a removable memory (e.g., a flash memory or other removable memory module) and a memory slot, and other removable storage units and interfaces that enable software and data to be transferred from the removable storage unit 1214 to the computing system 1200.

[0086] Computing system 1200 may also include a communication interface 1218. The communication interface 1218 can be used to enable software and data to be transferred between the computing system 1200 and external devices. Examples of the communication interface 1218 can include a network interface (such as an Ethernet (registered trademark) or other NIC card), a communication port (e.g., a USB port, a micro USB port, etc.), near field communication (NFC), and other similar communication interfaces. The software and data transferred via the communication interface 1218 can be in the form of signals that are electrical, electromagnetic, optical, or other signals that can be received by the communication interface 1218. These signals are provided to the communication interface 1218 via a channel 1220. The channel 1220 can carry signals and can be implemented using a wireless medium, a wire or cable, an optical fiber, or other communication media. Some examples of the channel 1220 can include a telephone line, a cellular phone link, an RF link, a Bluetooth link, a network interface, a local or wide area network, and other communication channels.

[0087] Computing system 1200 may further include an input / output (I / O) device 1222. Examples include, but are not limited to, a display, keypad, microphone, audio speaker, vibration motor, LED light, and other similar I / O devices. The I / O device 1222 can receive input from a user and display the output of computations executed by the processor 1202. As used herein, the terms “computer program product” and “computer-readable medium” may generally be used to refer to a medium such as, for example, the storage unit 1206, the storage device 1208, the removable storage unit 1214, or a signal on the channel 1220. These and other forms of computer-readable media may be involved in providing one or more sequences of one or more instructions to the processor 1202 for execution. Such instructions, generally referred to as “computer program code” (which may be grouped in the form of a computer program or other groups), when executed, enable the computing system 1200 to perform the features or functions of embodiments of the present invention.

[0088] In embodiments in which an element is implemented using software, the software is stored on a computer-readable medium and can be loaded into the computing system 1200 using, for example, the removable storage unit 1214, the media drive 1210, or the communication interface 1218. When the control logic (in this example, software instructions or computer program code) is executed by the processor 1202, the processor 1202 is caused to perform the functions of the present invention described herein.

[0089] As will be understood by those skilled in the art, the techniques described in the various embodiments above are not routine, not conventional, and not well understood in the art. The techniques described above provide a trained user for performing the body Activity First, via the graphical user interface (GUI) of the rendering device, a plurality of activity types OptionRender to the user. A plurality of Activity types Option each include a plurality of Activity . The present technology can then receive, in response to user input, a Activity option selected by the user from a plurality of Activity options. User input includes at least one of a gesture, a touch, or an audio command. The present technology can then, by at least one camera, Activity based on the selected Activity perform a real-time video of the user Capture . Each of the at least one camera captures the user's real-time video from a related predetermined angle. The real-time video includes the Capture postures and movements made by the user to perform the activity. The present technology can then extract an AI model based on the Flow option selected by the user. The AI model is configured to determine a deviation of the user from a plurality of correct movements associated with the Activity option based on the Activity expert's Target activity performance . The present technology can then process the user's real-time video in real time by the AI model to determine a set of Activity user Activity parameters based on the At that time activity of the user. The present technology can then overlay the user in the real-time video with a Performance skeleton model by the AI model. The pose skeleton model includes a plurality of key points based on the Performance . Each of the plurality of key points is overlaid on the corresponding Posture of the user in the real-time video. The present technology can then compare a set of Activity user performance parameters with a set of Joint activity performance parameters by the AI model. The Goal set of performance parameters is Target activity . The ActivityCorrespond to an expert. Next, the present technology can generate feedback about a user based on a comparison between a set of user performance parameters and Goal a set of activity performance parameters by an AI model. The feedback can include at least one of a corrective measure or a warning. The feedback can include at least one of visual feedback, auditory feedback, or tactile feedback. The present technology can then render the feedback on a rendering device by the AI model. Rendering the feedback can include overlaying one of at least one corrective measure on a pose skeleton model overlaid on the user's real-time video. Rendering the feedback further includes Warning displaying it on the GUI of the rendering device. Rendering the feedback further includes outputting auditory feedback to the user via a speaker.

[0090] In light of the above advantages and the technological advancements provided by the disclosed methods and systems, the claimed steps discussed above are not routine, not conventional, or not well understood in the art because the claimed steps enable the following solutions to existing problems in the prior art. Further, the claimed steps clearly result in an improvement in the functionality of the device itself because the claimed steps provide a technical solution to a technical problem.

[0091] This specification describes the body ActivityA method and system for training a user to perform are described. The illustrated steps are presented to describe the illustrated exemplary embodiments, and ongoing technological developments should be expected to change the way certain functions are performed. These examples are presented herein for illustrative purposes and not for limitation. Further, the boundaries of the functional components are arbitrarily defined herein for the convenience of explanation. Alternative boundaries can be defined as long as the specified functions and their relationships are properly performed. Alternatives (including equivalents, extensions, variations, and departures such as those described herein) will be apparent to those skilled in the art based on the teachings contained herein. Such alternative forms are within the scope and spirit of the disclosed embodiments.

[0092] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory where information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors that include steps or stages consistent with the embodiments described herein for causing the processor to perform. The term "computer-readable medium" is understood to include tangible items and to exclude carrier waves and transient signals, i.e., to be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, non-volatile memory, hard drives, CD-ROMs, DVDs, flash drives, hard disks, and any other known physical storage media.

[0093] The present disclosure and examples are merely illustrative, and it is intended that the true scope and spirit of the disclosed embodiments be shown by the following claims.

Claims

1. A method for training a user to perform a physical activity, comprising: rendering, via a graphical user interface (GUI) of a rendering device, a plurality of activity type options to the user, each of the plurality of activity type options comprising a plurality of activities; receiving, in response to a user input, an activity option selected by the user from the plurality of activity options, the user input comprising at least one of a gesture, a touch, an audio command, or a signal generated from an input device; capturing, by at least one camera, a real-time video of the user performing an activity based on the selected activity option, each of the at least one camera capturing the user's real-time video from a predefined associated angle, the real-time video comprising the postures and movement flow made by the user to perform the activity; extracting an AI model based on the activity option selected by the user, the AI model being configured to determine a deviation of the user from a plurality of correct movements related to the activity corresponding to the activity option based on the target activity performance of an activity expert; processing, in real time, the user's real-time video by the AI model to determine a set of user performance parameters based on the activity performance at that time; overlaying, by the AI model, a pose skeleton model on the user in the real-time video, the pose skeleton model comprising a plurality of key points based on the activity, each of the plurality of key points being overlaid on the corresponding joint of the user in the real-time video; comparing, by the AI model, the set of user performance parameters with a set of target activity performance parameters, the set of target activity performance parameters corresponding to an activity expert; Generating feedback about a user based on a comparison between a set of user performance parameters and a set of target activity performance parameters by an AI model, wherein the feedback comprises at least one of a corrective measure or a warning, and the feedback comprises at least one of visual feedback, auditory feedback, or tactile feedback; Rendering feedback on a rendering device by the AI model; Overlaying on a pose skeleton model overlaid on the user's real-time video at least one of the at least one corrective measure; Displaying a warning on the GUI of the rendering device; Outputting auditory feedback to the user via a speaker, including rendering; Temporarily stopping the display of the set of user performance parameters and the target activity performance, the display being temporarily stopped when the activity performance at that time deviates from the target activity performance by exceeding a predefined threshold performance of a predefined threshold time based on the comparison; Including; Overlaying the pose skeleton model on the user in the real-time video; Automatically adjusting and normalizing the pose skeleton model based on the user's current pose and estimated future distance with respect to the rendering device, as well as the user's current pose and estimated future pose and movement; The feedback includes generating a warning to the user; The warning; Instructions for correcting the user's current pose; Instructions for correcting the user's movement related to the user's current pose; Instructions for correcting the user's current position when the user is at least partially outside the field of view of at least one camera, further including; Method. The method according to claim 1, further comprising displaying, through the GUI of the rendering device, the set of user performance parameters, the set of target activity performance parameters, and the target activity performance of the activity expert. ​ ​ ​ The method according to claim 2, further comprising overlaying the target activity performance in real time over the user's then activity performance in the real-time video.

4. The set of user performance parameters includes the speed of the then activity performance, the number of completed repetitions, the overall completion of the activity circuit, third-party smart device information, the user's heart rate, the user's blood pressure, and the user's movement, and the set of target activity performance parameters includes the speed of the target activity performance, the target number of repetitions, the user's target heart rate, and the user's target movement. The method according to claim 1.

5. When capturing real-time video via at least one camera, rendering at least one of a trainer avatar corresponding to the target activity performance of an activity expert and a user avatar corresponding to the user's then activity performance, wherein the trainer avatar is a three-dimensional (3D) model of the activity expert, and further including rendering the user avatar as a multi-dimensional model of the user. The method according to claim 1.

6. Comparing at least one of the trainer avatar and the user avatar corresponding to the target activity performance with the user's then activity performance based on the activity type and activity. Displaying, via the rendering device and via the GUI, the set of user performance parameters, at least one of the trainer avatar and the user avatar corresponding to the target activity performance, and the user's then activity performance, wherein the then activity performance is overlaid and displayed in real time on at least one of the trainer avatar and the user avatar corresponding to the target activity performance. The method according to claim 5. The method according to claim 5.

7. Storing the real-time video received from the at least one camera in a database. The step of editing the real-time video based on one or more user commands of the user, wherein the user commands are at least one of a text command, a voice command, a touch command, or a visual gesture, and the one or more user commands include setting a start point of the real-time video, setting an end point of the real-time video, removing a background from the real-time video, assigning one or more tags to the real-time video, and sharing the real-time video with other users, and including at least one of The method according to claim 1.

8. Receiving a real-time video in real time from an activity expert corresponding to a target activity performance according to the user's current activity performance, Displaying in real time, through a GUI via a rendering device, a set of user performance parameters and the target activity performance of the activity expert, wherein the target activity performance is overlaid across the user's current activity performance in the real-time video, and the target activity performance is overlaid on top of the user's current activity performance in the real-time video, and further comprising The method according to claim 1.

9. Detecting an initial position of the user via at least one camera, Determining whether the detected initial position of the user matches an initial position mapped to at least one activity, Instructing the user to correct the initial position when the detected initial position does not match the initial position, and further comprising The method according to claim 1.

10. The user command for selecting the activity option includes at least one of a voice command, a touch gesture, an air gesture, an eye gesture, or a signal generated by an input device. The method according to claim 1.

11. A rendering device for training the user to perform a physical activity, A display device including a GUI (Graphical User Interface), wherein the GUI Render a plurality of activity type options to the user, each of the plurality of activity type options comprising a plurality of activities, receive an activity option selected by the user from the plurality of activity type options in response to user input, the user input comprising at least one of a gesture, a touch, an audio command, a display device configured to: at least one camera configured to capture a real-time video of the user performing an activity based on the selected activity option, each of the at least one camera capturing the user's real-time video from a related predefined angle, the real-time video being configured to include the poses and movement flows made by the user to perform the activity, a camera; a processor; a memory communicatively coupled to the processor, storing processor instructions, the processor instructions, when executed by the processor: extracting an AI model based on the activity option selected by the user, the AI model being configured to determine a deviation of the user from a plurality of correct movements related to the activity corresponding to the activity option based on the target activity performance of the activity expert, extracting; processing the user's real-time video in real time by the AI model, determining a set of user performance parameters based on the activity performance at that time, processing; overlaying a pose skeleton model on the user in the real-time video having the pose skeleton model by the AI model, the pose skeleton model comprising a plurality of key points based on the activity, each of the plurality of key points being overlaid on the corresponding joint of the user in the real-time video, overlaying; comparing, by the AI model, a set of user performance parameters with a set of target activity performance parameters, the set of target activity performance parameters corresponding to the activity expert, comparing; Generating feedback about a user based on a comparison between a set of user performance parameters and a set of target activity performance parameters by an AI model, the feedback comprising at least one of a corrective measure or a warning, the feedback comprising at least one of visual feedback, auditory feedback, or tactile feedback, Rendering feedback on a rendering device by an AI model, Overlaying on a pose skeleton model overlaid on a user's real-time video at least one of the at least one corrective measure, Displaying a warning on the GUI of the rendering device, Outputting auditory feedback to the user via a speaker, including rendering, A memory for causing a processor to execute, Including, When the activity performance at that time deviates from the target activity performance by exceeding a predefined threshold performance of a predefined threshold time based on the comparison, temporarily stopping the display of the set of user performance parameters and the target activity performance, To overlay the pose skeleton model on the user in the real-time video, the processor instructions, at runtime, cause the processor to automatically adjust and normalize the pose skeleton model based on the user's current pose and estimated future distance to the rendering device and the user's current pose and estimated future pose and movement, The feedback includes generating a warning to the user, The warning, Instructions for correcting the user's current pose, Instructions for correcting the user's movement related to the user's current pose, Instructions for correcting the user's current position when the user is at least partially outside the field of view of at least one camera, A rendering device.

12. The processor instructions cause the processor to display, through the GUI of the rendering device, a set of user performance parameters, a set of target activity performance parameters, and the target activity performance of an activity expert, Execute a real-time overlay of the target activity performance on the user's current activity performance in real-time video The rendering device according to claim 11 **Claim 13** When executed, the processor instructions cause the processor to When capturing real-time video via at least one camera, render at least one of a trainer avatar corresponding to the target activity performance of the activity expert and a user avatar corresponding to the user's current activity performance, wherein the trainer avatar is a three-dimensional (3D) model of the activity expert and the user avatar is a multi-dimensional model of the user Compare at least one of the trainer avatar and the user avatar corresponding to the target activity performance with the user's current activity performance based on the activity type and activity Display, via a rendering device and via a GUI, a user performance parameter set, at least one of a trainer avatar and a user avatar corresponding to the target activity performance, and the user's current activity performance, wherein the current activity performance is overlaid and displayed in real-time on at least one of the trainer avatar and the user avatar corresponding to the target activity performance. The rendering device according to claim 11 **Claim 14** When executed, the processor instructions cause the processor to Store the real-time video received from the at least one camera in a database Edit the real-time video based on one or more user commands of the user, wherein the user commands are at least one of a text command, a voice command, a touch command, or a visual gesture, and the one or more user commands are Setting a start point of the real-time video Setting an end point of the real-time video Removing the background from the real-time video Assigning one or more tags to the real-time video, and The rendering device according to claim 11, causing to perform steps including at least one of sharing real-time video with other users.

15. The processor instructions, when executed, cause the processor to in real time, receive real-time video from an activity expert corresponding to a target activity performance in response to the user's current activity performance; and display in real time, via the rendering device and via the GUI, the user performance parameter set and the target activity performance of the activity expert, the target activity performance being overlaid on the user's current activity performance in the real-time video. The rendering device according to claim 11.

16. The processor instructions, when executed, cause the processor to detect an initial position of a user via at least one camera; determine whether the detected initial position of the user matches an initial position mapped to at least one activity; and if the detected initial position does not match the initial position, instruct the user to correct the initial position. The rendering device according to claim 11.

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