Fractal Motion Analysis for Athletic Form Assessment
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Solution Overview
Problem
Conventional systems fail to assess the quality of movement patterns in artistic, athletic, or other activities, relying on quantity measurements and lacking intuitive visualization tools, which can lead to improper form and increased risk of injury.
Innovation Solution
Incorporation of fractal analysis and machine learning techniques to quantify and visualize human or animal movement patterns, using inertial sensors to project three-dimensional motion into two-dimensional projections for advanced machine vision analysis and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use quantity measurements (number of steps, distance, repetitions), then the measurement is simple and straightforward, but the system cannot assess the quality of the entire movement pattern
Solution Approach 1:
The patent transforms complex 3D movement data into 2D orbital projections, reducing dimensionality while preserving essential movement quality information. This allows comprehensive movement pattern assessment through simplified visual representations that maintain diagnostic value without requiring full 3D analysis complexity
Solution Approach 2:
The system creates simplified 2D copies (orbital diagrams) of the actual 3D movement patterns. These orbital projections serve as representative copies that capture the essential characteristics of movement quality without requiring analysis of the complete three-dimensional data set
2Loss of information
If conventional systems provide limited key measurements, then the system is easier to operate and interpret, but it cannot provide comprehensive feedback on movement quality
Solution Approach 1:
The patent merges multiple movement parameters (acceleration, velocity, position) into a single integrated orbital diagram. This consolidation preserves comprehensive movement information while presenting it in a unified visual format that is easier to interpret than multiple separate measurements
Solution Approach 2:
By projecting 3D movement onto 2D orbital planes, the system preserves rich movement information while creating a visually intuitive representation that is easier to interpret than traditional time-series graphs or multiple separate measurements
3Measurement precision
If the system uses 3D motion analysis, then the movement quality assessment is more accurate, but the visualization is less intuitive for users
Solution Approach 1:
The system projects 3D movement data onto 2D orbital planes, creating visualizations that are both mathematically rigorous and visually intuitive. The orbital diagrams maintain the precision of 3D analysis while presenting information in a format that is easier for users to interpret and understand
4Reliability
If the system provides real-time feedback, then users can maintain proper form and reduce injury risk, but the system requires complex processing and analysis capabilities
Solution Approach 1:
By transforming complex 3D movement data into 2D orbital projections, the system enables real-time processing and feedback with reduced computational complexity. The dimensionality reduction allows for faster analysis while maintaining the diagnostic information needed for injury prevention
Solution Approach 2:
The system implements real-time feedback by continuously analyzing orbital projections and providing immediate guidance on movement quality. This enables users to adjust their form in real-time, preventing injury while the reduced computational complexity of 2D analysis facilitates timely processing
Data Source
AI summary
A machine learning process for classifying human or animal motion, including the classification of repetitive movements of a human or an animal in order to assess the quality of athletic performance, artistic performance, form, or other quality of motion. The classification of the repetitive movements, in particular, provide an indication of movement dysfunctions.