Motion Feedback System Using Normalized Data
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Solution Overview
Problem
Home training programs lack effective methods to evaluate and provide feedback on user posture, leading to potential exercise inefficacy and physical injury due to incorrect posture.
Innovation Solution
A motion feedback system that captures user motion through a camera and pressure sensors, normalizes data to expert reference data, and provides feedback by comparing user and expert motions in real-time, ensuring correct posture alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If home training programs use pre-made videos or still images without posture evaluation, then the program is simple and easy to implement, but the user cannot receive feedback on posture correctness leading to exercise inefficacy or injury
Solution Approach 1:
The system captures user motion data through cameras and pressure sensors, compares it with expert reference data, and provides real-time feedback on posture correctness. This feedback mechanism enables users to correct their posture during exercise, improving safety and effectiveness without requiring complex manual evaluation
Solution Approach 2:
The system creates a digital copy of expert motion patterns through reference data and uses this copy as a standard for comparing user performance. By copying and storing expert postures as reference data, the system enables automated comparison and feedback without requiring expert presence
2Measurement precision
If the system collects and processes multiple types of data (motion data and pressure data) for comprehensive posture analysis, then the feedback accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The system merges motion data from cameras with pressure data from sensors to create a comprehensive posture evaluation. By combining multiple data sources, the system achieves more accurate posture detection than single-source systems, compensating for individual sensor limitations
Solution Approach 2:
The system performs preliminary normalization of collected data to map user data onto expert reference data frameworks before comparison. This preprocessing step organizes raw data into comparable formats, reducing the complexity of subsequent analysis and enabling efficient multi-modal data integration
3Adaptability or versatility
If the system normalizes user data to match expert reference data, then the comparability between user and expert motions is improved, but the data processing time increases
Solution Approach 1:
The system changes the parameters of user data through normalization, transforming raw motion and pressure data into standardized formats that match expert reference data. This parameter transformation enables direct comparison between users of different sizes, speeds, and styles with expert benchmarks
Data Source
AI summary
A motion feedback method is disclosed. The method includes: acquiring a frame in which a scene of the motion performed by a user is captured through a camera; acquiring motion data of the user by analyzing the captured frame; acquiring pressure data measured in response to the user's motion through a pressure sensor; generating comparison target data by normalizing the motion data and the pressure data so as to correspond to pre-prepared reference data according to the expert's motion; and comparing the reference data and the comparison target data to generate feedback data for the user's motion, and outputting the generated feedback data.