An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection

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.

Decision support system for effective action recognition of track and field sports using ant colony optimization

“…The experimental data revealed that the system can achieve an accuracy of about 80%. Lu et al [24] have developed a cost-effective system for the training and talent selection grounded on arti cial intelligence and the Internet of Things (AI + IoT). The proposed system can work on very less computational assets and can be employed in foot-driven sports.…”

Lightweight Football Motion Recognition and Intensity Analysis Using Low-Cost Wearable Sensors

“…Although image analysis techniques can be used in the sports research field to make more accurate identification of athletes' behavior. However, this technology has some disadvantages in the development of the motion scene: the equipment cost is expensive, the system is vulnerable to the influence of the external environment, and relevant visual algorithms and image processing depend on a large number of hardware conditions [ 3 ]. With the popularity of IoT applications, sensor technology is developing rapidly, among which inertial microelectromechanical systems (MEMS) sensors are the most widely used.…”