Movement Assessment Model for Fast, Personalized Pose Feedback
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Solution Overview
Problem
Conventional motion capture technologies face challenges in accurately evaluating human movements due to variability in performance and body types, and the processing limitations of computing machinery make commercial applications impractical.
Innovation Solution
An automated system using machine learning techniques analyzes labeled video and audio feedback to assess and improve movement performance by identifying key points, adjusting feature ranges, and providing personalized feedback through a convolutional neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional motion capture technologies are used to evaluate human movements, then measurement capability is provided, but processing time is excessively long (weeks) and manufacturing precision is insufficient due to variability in performance and body types
Solution Approach 1:
The patent replaces conventional mechanical motion capture systems with a machine learning-based automated assessment system. The system uses image processing and neural networks to automatically evaluate movements, eliminating the need for complex mechanical capture equipment and manual analysis, thereby reducing processing time from weeks to minutes while improving consistency and precision across different body types
Solution Approach 2:
The patent transforms the assessment approach by changing from fixed mechanical measurement parameters to adaptive machine learning parameters. The system learns optimal evaluation parameters from training data, allowing it to adjust to different body types and movement styles dynamically, improving measurement precision without increasing processing time
2Productivity
If conventional motion capture technologies are used, then movement data can be captured, but device complexity and processing requirements make commercial applications impractical
Solution Approach 1:
The patent extracts the essential evaluation functionality from complex conventional motion capture systems. By isolating and automating only the critical assessment tasks through machine learning, the system eliminates unnecessary complexity while maintaining commercial viability and improving productivity
3Productivity
If automated assessment is implemented to reduce processing time, then productivity improves, but measurement precision may be compromised without proper model fitting
Solution Approach 1:
The patent performs preliminary model fitting and parameter optimization during an offline training phase. By pre-processing and preparing the assessment models in advance, the system achieves both high processing speed during actual use and maintains measurement precision through carefully fitted models, resolving the contradiction between productivity and accuracy
Data Source
AI summary
The present technology provides systems, methods and computer program instructions implementing an automated technology for automated creation of movement assessments from labeled video and continually learning audio, video or other feedback for use with machine learning techniques enable program processes to learn more effective feedback mechanisms to achieve desired results (e.g., reduce errors, improve form, duration, speed, and so forth) of motions and poses comprising tasks being taught or guided. In implementations machine learning techniques enable program processes to learn more effective feedback mechanisms to achieve desired results (e.g., reduce errors, improve form, duration, speed, and so forth) of motions and poses comprising tasks being taught or guided.