Biomechanical Feedback via Computer Vision and AI Analysis
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Solution Overview
Problem
Current computing systems for exercise and fitness activities provide limited ability to compare user performance to benchmarks and offer detailed feedback for corrections, despite their capability to collect extensive data.
Innovation Solution
The system utilizes computer vision and artificial intelligence to capture user performance, assess deviations from ideal biomechanics, and provide personalized feedback, including corrective actions and exercise programs, based on a corpus of knowledge related to the activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general monitoring of exercise activity and biometric data is provided, then basic feedback is given, but detailed feedback for performance comparison and corrections is limited
Solution Approach 1:
The patent introduces a computer vision system as an intermediary that captures visual data of user exercise performance. This visual data serves as a mediator between the user's physical actions and the feedback generation system, enabling detailed biomechanical analysis without requiring complex sensor arrays on the user's body. The vision system processes images to extract movement parameters, providing a bridge that enables precise measurement while keeping the overall system architecture manageable.
Solution Approach 2:
The patent replaces traditional mechanical sensors and wearables that directly contact the user's body with a non-contact computer vision system. Instead of using mechanical sensors to measure biomechanics, the system uses optical fields (cameras) to capture movement, then processes this visual information through image processing algorithms to derive biomechanical parameters. This substitution eliminates the need for complex mechanical sensing hardware while achieving comparable or superior measurement precision.
2Loss of information
If extensive data is collected during exercise activities, then comprehensive information is available, but the ability to provide detailed performance comparison is limited
Solution Approach 1:
The patent extracts and isolates specific critical biomechanical parameters from the extensive data collected during exercise. Rather than processing all raw data equally, the system identifies and extracts key parameters such as joint angles, movement velocity, and posture deviations that are most relevant to performance assessment. This extraction process filters the abundant data to leave only the essential information needed for meaningful feedback, improving both information utilization and feedback generation efficiency.
Solution Approach 2:
The system performs preliminary analysis by comparing extracted biomechanical parameters against pre-established ideal performance benchmarks and standards before generating feedback. This preliminary comparison prepares the data in advance, identifying deviations from optimal performance so that feedback can be generated more efficiently and accurately. The preliminary action of benchmark comparison enables the system to quickly determine what needs to be communicated to the user without processing all raw data in real-time.
Data Source
AI summary
Embodiments for providing activity feedback are provided. Information associated with a user performing an activity is received. A user biomechanical representation is generated based on the received information. A corpus associated with the activity is analyzed. An ideal biomechanical representation is generated based on the analyzing of the corpus associated with the activity. The user biomechanical representation is compared to the ideal biomechanical representation. Feedback for the user is generated based on the comparison of the user biomechanical model to the ideal biomechanical representation.