An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection
Motion tracking in different fields (medical, military, film, etc.) based on microelectromechanical systems (MEMS) sensing technology has been attracted by world's leading researchers and engineers in recent years; however, there is still a lack of research covering the sports field. In this st...
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doaj-579594e8be054bf0b93c1dc4db5b15092021-07-05T00:02:20ZengHindawi LimitedJournal of Healthcare Engineering2040-23092021-01-01202110.1155/2021/9958256An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent SelectionShanshan Lu0Xiao Zhang1Jiangqing Wang2Yufan Wang3Mengjiao Fan4Yu Zhou5Department of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Industrial Engineering & ManagementDepartment of Industrial Engineering & ManagementCollege of Computer Science and Software EngineeringMotion tracking in different fields (medical, military, film, etc.) based on microelectromechanical systems (MEMS) sensing technology has been attracted by world's leading researchers and engineers in recent years; however, there is still a lack of research covering the sports field. In this study, we propose a new AIoT (AI + IoT) paradigm for next-generation foot-driven sports (soccer, football, takraw, etc.) training and talent selection. The system built is cost-effective and easy-to-use and requires much fewer computational resources than traditional video-based analysis on monitoring motions of players during training. The system built includes a customized wireless wearable sensing device (WWSDs), a mobile application, and a data processing interface-based cloud with an ankle attitude angle analysis model. Eleven right-foot male participators wore the WWSD on their ankle while each performed 20 instances of different actions in a formal soccer field. The experimental outcome demonstrates the proposed motion tracking system based on AIoT and MEMS sensing technologies capable of recognizing different motions and assessing the players’ skills. The talent selection function can partition the elite and amateur players at an accuracy of 93%. This intelligent system can be an emerging technology based on wearable sensors and attain the experience-driven to data-driven transition in the field of sports training and talent selection and can be easily extended to analyze other foot-related sports motions (e.g., taekwondo, tumble, and gymnastics) and skill levels.http://dx.doi.org/10.1155/2021/9958256 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shanshan Lu Xiao Zhang Jiangqing Wang Yufan Wang Mengjiao Fan Yu Zhou |
spellingShingle |
Shanshan Lu Xiao Zhang Jiangqing Wang Yufan Wang Mengjiao Fan Yu Zhou An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection Journal of Healthcare Engineering |
author_facet |
Shanshan Lu Xiao Zhang Jiangqing Wang Yufan Wang Mengjiao Fan Yu Zhou |
author_sort |
Shanshan Lu |
title |
An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection |
title_short |
An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection |
title_full |
An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection |
title_fullStr |
An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection |
title_full_unstemmed |
An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection |
title_sort |
iot-based motion tracking system for next-generation foot-related sports training and talent selection |
publisher |
Hindawi Limited |
series |
Journal of Healthcare Engineering |
issn |
2040-2309 |
publishDate |
2021-01-01 |
description |
Motion tracking in different fields (medical, military, film, etc.) based on microelectromechanical systems (MEMS) sensing technology has been attracted by world's leading researchers and engineers in recent years; however, there is still a lack of research covering the sports field. In this study, we propose a new AIoT (AI + IoT) paradigm for next-generation foot-driven sports (soccer, football, takraw, etc.) training and talent selection. The system built is cost-effective and easy-to-use and requires much fewer computational resources than traditional video-based analysis on monitoring motions of players during training. The system built includes a customized wireless wearable sensing device (WWSDs), a mobile application, and a data processing interface-based cloud with an ankle attitude angle analysis model. Eleven right-foot male participators wore the WWSD on their ankle while each performed 20 instances of different actions in a formal soccer field. The experimental outcome demonstrates the proposed motion tracking system based on AIoT and MEMS sensing technologies capable of recognizing different motions and assessing the players’ skills. The talent selection function can partition the elite and amateur players at an accuracy of 93%. This intelligent system can be an emerging technology based on wearable sensors and attain the experience-driven to data-driven transition in the field of sports training and talent selection and can be easily extended to analyze other foot-related sports motions (e.g., taekwondo, tumble, and gymnastics) and skill levels. |
url |
http://dx.doi.org/10.1155/2021/9958256 |
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