Athlete Training Assistance System Based on Digital Twin Technology and Wireless Body Area Network

Systematic and scientific training is an essential aspect of improving athletes' performance and has garnered significant attention. Athletes are continuously seeking new technologies to aid in training and prevent injuries. Digital twin technology, which integrates the Internet of things (IoT), big data, and artificial intelligence (AI), shows potential for providing athletes with continuous monitoring and in-depth analysis to support their training. This study employs wireless body area network (WBAN) and digital twin (DT) technology to design a novel system for assisting athletes training. On the hardware level, the system utilizes a WBAN with multiple sensors to collect physical and physiological data from athletes. On the software level, the system comprises three modules: motion capture, behavior recognition, and motion correction modules. These modules enable the digital twin to reflect the athlete's movements, behaviors, and physiological attributes in real-time, helping athletes understand their physical condition, correct erroneous movements, and prevent potential health hazards. Furthermore, this study demonstrates how to construct a digital twin human, providing a method that can be transplanted to other scenarios.