AI Trainer System for Real-Time Home Exercise Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Individuals face challenges in consistently performing physical activities correctly and effectively at home, lacking expert guidance and motivation, which can lead to injuries and decreased motivation over time.
Innovation Solution
A system and method utilizing AI models to capture and coordinate physical activities of multiple users in real-time, providing feedback through visual, aural, or haptic means, and allowing users to perform activities under expert guidance without the need for physical trainers or dedicated facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users perform physical activities at home without expert guidance, then convenience and accessibility are improved, but performance accuracy and injury risk worsen
Solution Approach 1:
The system creates a virtual copy of an expert trainer through AI models that capture and replicate expert movement patterns, posture requirements, and instructional techniques. This virtual trainer is then deployed to provide continuous guidance to users at home, eliminating the need for physical presence while maintaining expert-level instruction quality.
Solution Approach 2:
The patent replaces the mechanical system of physical trainer presence with an automated AI-based monitoring and feedback system. Computer vision algorithms, sensors, and machine learning models substitute for human trainer observations and corrections, enabling automated performance analysis and real-time feedback without requiring physical expertise to be physically present.
2Manufacturing precision
If physical trainers are present to provide guidance, then performance accuracy is improved, but cost and accessibility worsen
Solution Approach 1:
The system enables users to receive expert guidance through self-service automation. The AI trainer autonomously monitors user performance, analyzes movement quality, provides real-time corrections, and adjusts instruction without requiring human intervention. This self-service capability eliminates the need for expensive human trainers while maintaining high performance accuracy through automated computer vision and machine learning algorithms.
Solution Approach 2:
The patent transforms the delivery mechanism of trainer expertise from a human-based service model to a digital parameter-based system. Expert knowledge is encoded as adjustable parameters in AI models, including movement thresholds, performance metrics, and feedback rules. These parameters can be dynamically modified to suit different users and activities, providing scalable expertise without proportional cost increases.
3Productivity
If constant monitoring is provided to maintain motivation, then user engagement is improved, but system complexity worsens
Solution Approach 1:
The system implements continuous feedback loops where the AI trainer monitors user performance in real-time and provides immediate verbal, visual, or haptic feedback. This feedback includes form corrections, encouragement, performance metrics, and adaptive instruction adjustments. The feedback mechanism maintains user engagement by simulating the motivational presence of a human trainer while using automated sensing and processing systems.
Data Source
AI summary
The present disclosure relates to system and method for coordinating and providing overall feedback for one or more users performing one or more physical activities at one or more locations. The feedback may be generated as AI feedback or human feedback. The method involves data capturing and coordinating the physical activities of the multiple users. The information to be captured is regarding performance activity of the multiple users and processing the same information in real time using AI assisted model. The method includes comparing each user's activity performance data including various performance parameters having a set of target activity performance parameters. The method includes generating feedbacks based on the comparison of the performance parameters. The feedbacks generated are shared with the users and rendered on the multimedia output device available to the users. The method includes sending the feedback to external portals via corresponding Application Programming Interfaces (APIs).