Exercise Motion Analysis System for Objective Runner Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Runners face challenges in objectively selecting suitable shoes and training methods that align with their individual running styles, leading to potential performance enhancement and injury risk, as current methods rely on subjective recommendations rather than objective analysis.
Innovation Solution
An exercise motion analysis system that inputs motion data to classify users into runner types through cluster analysis, using machine learning to recommend appropriate wearing tools and training methods based on objective performance indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If runners select shoes and training methods based on subjective recommendations from acquaintances or published information, then the selection process is simple and accessible, but the objective evaluation of whether the selected products truly fit their running styles becomes difficult
Solution Approach 1:
The patent replaces subjective mechanical recommendation systems with an automated computer-based analysis system that uses machine learning algorithms to objectively evaluate running motion data and recommend suitable shoes and training methods, thereby achieving both ease of use and objective accuracy
Solution Approach 2:
The patent introduces motion data as an intermediary objective measure between the runner and the selection process. By capturing and analyzing actual motion parameters (cadence, ground contact time, vertical stiffness, etc.), the system provides an objective basis for evaluation that mediates between subjective feeling and objective suitability
2Ease of operation
If runners rely on subjective feelings during actual wear, then the process is simple and requires no additional equipment, but the ability to realize whether products have led to enhancement of performance becomes limited
Solution Approach 1:
The patent implements a feedback mechanism where motion data collected during running is analyzed to provide objective feedback on performance parameters. The system compares pre-and-post intervention data to verify whether the selected shoes or training methods have led to actual performance enhancement, providing reliable verification while maintaining ease of use through automated analysis
3Measurement precision
If the system classifies runners into multiple types using cluster analysis on motion data, then the objectification of runner types is achieved without artificial or uniform dividing methods, but the complexity of the analysis system increases
Solution Approach 1:
The patent transforms the complex problem of runner classification into a manageable form by changing the approach from subjective categorization to objective parameter-based clustering. By using machine learning algorithms that automatically identify patterns in motion parameters (cadence, ground contact time, vertical stiffness, etc.), the system achieves accurate classification while managing complexity through automated computation rather than manual analysis
Data Source
AI summary
In an exercise motion analysis system, an input unit inputs motion data corresponding to multiple types of motion parameters indicating motion states of a user who is running. A selection unit selects, from among multiple types of wearing tools, a type of a wearing tool commensurate with a runner type in which motion data of the user is classified, among multiple runner types classified in advance based on cluster analysis on motion data of multiple people. An output unit outputs the type of a wearing tool selected as a wearing tool of which wearing is to be recommended to the user.