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Aided Evaluation of Motion Action Based on Attitude Recognition.

Qi Wang^1,2, Qing-Ming Wang¹

¹Institute of Mechanical and Dynamic Engineering, East China University of Science and Technology, Shanghai 200237, China.

Journal of Healthcare Engineering

|March 21, 2022

Summary

This study introduces a new AI system for athletes to analyze sports movements using deep learning and video processing. The system accurately evaluates posture and provides data for better performance and fewer injuries.

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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Published on: December 11, 2015

Area of Science:

Computer Science
Sports Science
Artificial Intelligence

Background:

Athletes struggle to obtain accurate movement data due to equipment and human errors.
Lack of precise data hinders professional guidance and posture correction, impacting athletic success.

Purpose of the Study:

To develop an AI-powered auxiliary evaluation system for sports movements.
To leverage deep learning and human pose recognition for accurate athlete data analysis.

Main Methods:

Utilized the OpenPose open-source library for human pose recognition and joint coordinate extraction.
Employed convolution neural networks and video processing for movement analysis and comparison against a standard motion database.
Calculated Euclidean distance to quantify action standardization.

Main Results:

The system accurately identifies key human posture points and calculates joint angle data.
Movement evaluation confirmed action amplitude conformity to standard data.
Achieved a 98.7% correct recognition rate for attitude recognition, surpassing previous methods by 2.3%.

Conclusions:

The developed sports action assistant evaluation system effectively addresses challenges faced by athletes.
The system provides accurate data for understanding body posture and movement, aiding performance improvement.
Further optimization and research are recommended for system testing and operation.