Motion Analysis System Using Partial Motion Separation
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Solution Overview
Problem
Conventional motion analysis techniques in athletic disciplines rely on subjective instructor judgment, which is time-consuming and costly, lacking objective precision.
Innovation Solution
A method and system for generating motion data sets from user images, separating partial motions, and determining relevance between user and reference motions using machine learning models, enabling precise analysis of user motions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an instructor personally evaluates user motions based on acquired images, then motion evaluation can be performed, but the process becomes time-consuming and costly
Solution Approach 1:
The system enables self-service motion evaluation by automatically comparing user motions against reference motions using image processing and pattern recognition algorithms. The computer extracts motion patterns from images and performs evaluation without requiring instructor intervention, thus eliminating time loss while maintaining evaluation precision.
Solution Approach 2:
The patent replaces the mechanical system of manual instructor evaluation with an automated computer-based system. The computer processes images, extracts motion patterns, and compares them against reference patterns algorithmically, substituting human physical evaluation with automated computational analysis to reduce time consumption.
2Measurement precision
If an instructor personally evaluates user motions, then motion analysis can be conducted, but the cost increases due to instructor requirements
Solution Approach 1:
The system performs self-service motion analysis by automatically processing images and comparing motions against stored reference patterns. This eliminates the need for expensive instructor services while maintaining analysis precision through automated pattern recognition and comparison algorithms.
Solution Approach 2:
The system creates and uses reference motion patterns as templates for comparison. By storing ideal motion patterns and comparing user motions against these copies, the system achieves consistent precision evaluation without requiring expensive expert instructors for each evaluation instance.
3Ease of operation
If instructor judgment is used for motion evaluation, then evaluation can be performed, but subjectivity reduces reliability
Solution Approach 1:
The patent replaces subjective human judgment with objective computer-based pattern recognition. The system extracts motion patterns from images and compares them against reference patterns using algorithms, eliminating subjectivity and improving evaluation reliability while maintaining ease of operation through automated processing.
Solution Approach 2:
The system provides objective feedback by comparing user motions against stored reference patterns and generating evaluation results based on pattern matching accuracy. This automated feedback mechanism eliminates subjective bias and improves reliability by consistently applying the same evaluation criteria to all users.
Data Source
AI summary
According to one aspect of the present invention, there is provided a method for analyzing a user's motion, the method comprising the steps of: generating a motion data set for a motion of a user on the basis of at least one image of the motion of the user; determining a partial motion data set for a partial motion of the user by separating the motion data set by partial motions of the user; and determining a first relevance between the partial motion of the user and a reference partial motion by comparing the partial motion data set with a reference partial motion data set determined by separating a reference motion data set by reference partial motions.