REAL-TIME INJURY RISK ASSESSMENT AND EARLY WARNING FOR SOCCER PLAYERS UTILIZING SENSORS AND MACHINE LEARNING

Abstract

In modern soccer, player health and injuries are critical to team competitiveness and sustainability. Traditional injury risk assessment methods have limitations, but with the development of sensor technology and machine learning algorithms, new possibilities for real-time injury risk assessment of players are provided. The application of IoT and AI technologies is driving the development of intelligence, and combining them to monitor the physiological state and athletic injurie load of players in real time with the help of wearable sensors can provide objective and accurate data support. Using machine learning algorithms, sensor data can be analyzed to build a prediction model for player injury risk. Individualized injury risk assessment for different players becomes possible. The soccer movement recognition and analysis combines sensor data to design a framework for soccer movement recognition and assessment, in order to achieve real-time monitoring and risk assessment of players during matches and training, and to help minimize the risk of player injuries.