Abstract

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.

Highlights

  • Recent trends in smart wearable technologies based on the Internet of ings (IoT) have opened up a large number of applications [1], which involve the recognition of sports activities [2,3,4]

  • Erefore, an IoT system based on wearable sensing devices (WSDs) is proposed in this work to provide objective feedback to coaches and soccer players after or during a training session to help them improve their skills. e proposed IoT system consists of wearable devices, a mobile device, and a data processing platform based cloud

  • An AIoT system to recognize different soccer motions and assess the skill levels of soccer players was proposed in this paper. e proposed IoT system consists of wearable devices, a mobile device, and a cloud-based data processing platform

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Summary

Research Article

We propose a new AIoT (AI + IoT) paradigm for next-generation foot-driven sports (soccer, football, takraw, etc.) training and talent selection. E 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. E 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. Is 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 extended to analyze other foot-related sports motions (e.g., taekwondo, tumble, and gymnastics) and skill levels Eleven right-foot male participators wore the WWSD on their ankle while each performed 20 instances of different actions in a formal soccer field. e 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. e talent selection function can partition the elite and amateur players at an accuracy of 93%. is 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 extended to analyze other foot-related sports motions (e.g., taekwondo, tumble, and gymnastics) and skill levels

Introduction
Related Work
Mobile device
Pending data
Passing matrices
Findings
Conclusion and Discussion
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