Martial Arts Training Prediction Model Based on Big Data and MEMS Sensors

Shisen Li,Guoliang Yuan,Chao Liu,Shaukat Ali

doi:10.1155/2021/9993916

Shisen Li, Guoliang Yuan + Show 2 more

Open Access

https://doi.org/10.1155/2021/9993916

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Abstract

In martial arts teaching and sports training, the accurate capturing and analysis of martial arts athletes’ posture is conducive to accurately judge sports postures, as well as correcting sports movements in a targeted manner, further improving martial arts athletes’ performance and reducing physical damage. The manufacturing level of MEMS sensors continues to improve, and status perception of assembly objects is becoming more and more abundant and accurate. The shape is small and can be worn, and data can be collected continuously without obstacles. The price is relatively low, the privacy protection is strong, and the advantages are clear and prominent. A considerable number of technicians choose to use MEMS sensors as the main tool for human behavior detection data collection. Therefore, this article designs multiple MEMS inertial sensors to form a human body lower limb capture device, and its core components are composed of accelerometer, gyroscope, and magnetometer. In order to make the obtained acceleration value, angular velocity value, and magnetometer value accurately reflect the movement state of the lower limb structure, different data fusion algorithms and magnetometer ellipsoid fitting calibration algorithms are studied to realize the calculation of the posture angle of each joint point and obtain martial arts posture big data. In addition, through big data analysis, this article designs a martial arts training performance and injury risk prediction model, which can provide guidance and suggestions for martial arts teaching tasks.

Highlights

In martial arts teaching and sports training, the accurate capturing and analysis of martial arts athletes’ posture is conducive to accurately judging sports postures, as well as correcting sports movements in a targeted manner, further improving martial arts athletes’ performance and reducing physical damage
The data processing process of the optical work capture system includes identification, tracking, and coordinate calculation. e calculation workload is relatively large, the related equipment is expensive, the optical conditions are demanding, and the effect of motion capture in the occluded state is poor. e MEMS-type inertial sensor motion capture analysis equipment can overcome the problem of insufficient test accuracy of the optical system in the low-light environment and motion blocking state, and the cost of the equipment is low, the test accuracy is high, and the response speed is fast, which is conducive to real-time motion capture
(ii) Aiming at martial arts training performance and injury risk prediction, this paper proposes a big data analysis method and designs a support vector regression model to achieve more accurate prediction

Summary

Introduction

In martial arts teaching and sports training, the accurate capturing and analysis of martial arts athletes’ posture is conducive to accurately judging sports postures, as well as correcting sports movements in a targeted manner, further improving martial arts athletes’ performance and reducing physical damage. Erefore, this article designs multiple MEMS inertial sensors to form a human body lower limb capture device, and its core components are composed of accelerometer, gyroscope, and magnetometer.

Results

Conclusion