Low-latency personalized fitness recommendation using muscle load method and system

A system, for personalized fitness recommendation workout comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive a video feed in real time and an user selected fitness activity along with user information, wherein the video feed comprises motion information of user per time instance captured via an image capturing device, and a set of exercises corresponding to the user selected fitness activity;
• dynamically detect from the video feed, a current posture from body motion three dimensional (3D) coordinates of the user starting from movelet and ending movelet, and joint-wise range of motion angles using a predefined muscle library;
• compute a muscle load for the current posture of the user selected fitness activity, wherein an alert is raised if the muscle load exceeds a predefined muscle load for target muscle group;
• predict a target posture for the user from the current posture in reference to the user selected fitness activity using (i) the muscle load, (ii) the user profile and raising an alert for incorrect target posture performed by the user, wherein the target posture is obtained from a exercise repository based on a matching criteria from pretrained biomechanical models, a correlation between the current posture of the user and the user selected fitness activity;
• and
• recommend a personalized fitness workout for the user by sequentially retrieving next matching exercise(s) for the target posture from the exercise repository, and wherein for absence of matching exercise(s) in the exercise repository a generative artificial intelligence (GenAI) fitness model synthetically generates a list of exercises recommended by AI for the user.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving, a video feed in real time and an user selected fitness activity along with user information, wherein the video feed comprises motion information of user per time instance captured via an image capturing device, and a set of exercises corresponding to the user selected fitness activity;
• dynamically, detecting from the video feed, a current posture from body motion three dimensional (3D) coordinates of the user starting from movelet and ending movelet, and joint-wise range of motion angles using a predefined muscle library;
• computing, a muscle load for the current posture of the user selected fitness activity, wherein an alert is raised if the muscle load exceeds a predefined muscle load for target muscle group;
• predicting, a target posture for the user from the current posture in reference to the user selected fitness activity using (i) the muscle load, (ii) the user profile and raising an alert for incorrect target posture performed by the user, wherein the target posture is obtained from a exercise repository based on a matching criteria from pretrained biomechanical models, a correlation between the current posture of the user and the user selected fitness activity;
• and
• recommending, a personalized fitness workout for the user by sequentially retrieving next matching exercise(s) for the target posture from the exercise repository, and wherein for absence of matching exercise(s) in the exercise repository a generative artificial intelligence (GenAI) fitness model synthetically generates a list of exercises recommended by AI for the user.