Biomechanical Score Generation from Video for Individualized Training
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Solution Overview
Problem
Current movement analysis methods and systems in athletics are limited by the need for specialized equipment, extensive measurements, and expert interpretation, failing to provide objective, quantitative outputs for qualitative aspects of an athlete's movement and individualized training programs.
Innovation Solution
A system comprising a data collection device and a processor configured to receive user characterization input, capture evaluation movements, and automatically output biomechanical scores and a comprehensive score, generating an individualized training program to improve physical ability without requiring specialized equipment or expert analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized motion capture equipment and sensors are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses video recordings as a copy of the actual movement to extract biomechanical data. Instead of requiring specialized motion capture equipment, the system processes standard video footage to obtain movement characteristics, thereby eliminating complex hardware while maintaining analysis capability
Solution Approach 2:
The patent replaces mechanical measurement systems (motion capture equipment, sensors) with a computational approach using image processing and machine learning algorithms. The mechanical system is substituted by an information processing system that extracts biomechanical data from video frames
2Measurement precision
If specialized equipment and expert interpretation are used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically extracting biomechanical features and generating training recommendations without requiring expert intervention. The machine learning model autonomously processes video data, identifies movement patterns, and produces actionable insights, eliminating the need for specialized operator knowledge
Solution Approach 2:
The patent pre-trains machine learning models with extensive biomechanical data and expert knowledge before deployment. This preliminary action embeds expert interpretation capabilities into the algorithm, allowing the system to operate autonomously without requiring users to have expert-level understanding
3Measurement precision
If comprehensive movement analysis is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-processing video footage during recording and extracting key frame data in real-time. Biomechanical features are identified from selected key frames rather than analyzing every frame, significantly reducing processing time while maintaining measurement accuracy
Solution Approach 2:
The patent applies partial action by focusing analysis on critical movement phases and key anatomical landmarks rather than comprehensively analyzing every aspect of movement. This selective approach extracts sufficient biomechanical information without requiring exhaustive analysis of all movement parameters
4Productivity
If quantitative outputs are provided, then productivity is improved, but loss of information deteriorates
Solution Approach 1:
The patent transforms qualitative movement information into quantitative parameters by extracting biomechanical features (angles, velocities, accelerations) from video data. This parameter transformation converts descriptive movement quality into measurable metrics that can be objectively analyzed and tracked over time
Solution Approach 2:
The system uses biomechanical scores as an intermediary between raw video data and training recommendations. These scores aggregate multiple movement parameters into meaningful metrics that preserve essential movement quality information while enabling efficient processing and actionable insights
Data Source
AI summary
Methods and systems are provided for capturing and analyzing movement. In one example the system includes a user device, a data collection device communicatively coupled to the user device, a processor configured with instructions stored on non-transitory memory that, when executed, cause the processor to: receive user characterization input of a subject; receive, from the data collection device, one or more of one or more evaluation movements of the subject captured by the data collection device, and measured movement features; automatically output one or more biomechanical scores, each biomechanical score based on more than one measured movement features; output a comprehensive score based on the one or more biomechanical scores, the comprehensive score indicative of a physical ability of the subject; and output a training program, where the training program is individualized to the subject and configured to increase the comprehensive score of the subject.