Wearable Gait Practice Feedback for Personalized Running Menus
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Solution Overview
Problem
Existing gait analysis systems fail to provide personalized practice menu information for athletes to improve their running ability and do not consider injury risk factors.
Innovation Solution
A practice support system that utilizes a wearable sensor to collect log data and evaluation data, creating a personalized model to suggest practice menu information and adjust posture to reduce injury risk, using a regression model for susceptibility analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a three-dimensional measurement device is used to measure posture coordinates, then objective evaluation of gait is achieved, but personalized practice menu information is not provided
Solution Approach 1:
The system implements a feedback loop where gait evaluation results are used to automatically generate personalized practice menu information. The evaluation data from the three-dimensional measurement device feeds back into the recommendation generation, creating a closed-loop system that continuously improves personalization based on actual user performance.
Solution Approach 2:
The system changes the output parameters from mere measurement data to actionable practice recommendations. By transforming the evaluation results into personalized practice menu information with specific exercises, durations, and frequencies, the system adapts the same measurement infrastructure to serve different functional purposes.
2Measurement precision
If gait analysis is performed to evaluate posture, then objective evaluation is provided, but practice recommendations tailored to individual athletes are not generated
Solution Approach 1:
The system extracts actionable insights from the gait analysis data by identifying specific posture deviations and performance patterns. From the raw measurement data, it extracts meaningful information such as stride length variations, posture angles, and rhythm patterns that can be translated into specific practice recommendations.
Solution Approach 2:
The system introduces an intermediary processing layer between gait analysis and practice recommendation generation. This intermediary component translates medical/scientific gait evaluation terminology into sport-specific practice instructions, making the information actionable for athletes while maintaining the precision of the original analysis.
3Adaptability or versatility
If comprehensive practice menu information is generated for each athlete, then personalization is improved, but system complexity increases
Solution Approach 1:
The system segments the practice menu generation into modular components: gait evaluation module, data analysis module, recommendation generation module, and feedback module. Each segment handles a specific function independently, making the overall complex system manageable and easier to implement while maintaining high personalization capability.
Data Source
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Figure 3A~3B
AI summary
A practice support apparatus (30) comprises: an acquisition unit (31) that acquires log data and evaluation data indicating an evaluation of the log data regarding exercise of a user (1) from a sensor (10) worn by the user (1); a record controller (33) that records the log data and the evaluation data over time for each user (1); a model creation unit (34) that creates model information on an exercise pattern for each user (1) on the basis of the log data and the evaluation data; and a suggestion unit (35) that suggests practice menu information for the user (1) on the basis of the created model information.