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Faculty Supervisor

Vincent Chen

Research Area

Sports Science

Abstract

The integration of Artificial Intelligence (AI) with wearable sensors has revolutionized soccer training. Real-time data on various physical, physiological, and biomechanical metrics such as acceleration, heart rate, and movement patterns are collected by the Local Position Measurement (LPM) and Electronic Performance and Tracking Systems (EPTS) sensors. By inputting these data into Machine Learning (ML) algorithms, specifically Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Decision Trees (DTs), coaches can precisely analyze performance, manage injury risks, and develop personalized training plans for their athletes. Additionally, coaches can optimize training intensity, improve tactical decisions, and reduce injury risks. Furthermore, these sensors track recovery features such as sleep quality, stress levels, and mood, resulting in a comprehensive approach to managing athletes. As AI and wearable sensors continue to improve, their applications in soccer training will also continue to advance, resulting in improvements in player development, injury prevention, and team dynamics. The present paper discusses the role of AI integration with wearable sensors in soccer training by emphasizing their effectiveness in improving player conditions and team success.

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