CNN sensor based motion capture system application in basketball training and injury prevention

Basketball is a high-intensity sport, and sports injuries often occur. Therefore, how to monitor the sports status of basketball players in real time, discover and prevent the occurrence of sports injuries in time, has become an urgent problem for athletes and coaches. In this paper, a motion capture system based on CNN sensor is proposed. Through the application of sensor, real-time monitoring of athletes' motion state, the system can collect athletes' movement track, speed, acceleration, stride frequency, heart rate, energy consumption and other parameters in real time. By analyzing the movement data, it can timely warn and deal with the occurrence of sports injuries. In this paper, convolutional neural network (CNN) is used to process and analyze motion data, so that the motion capture system has higher precision and accuracy. Through the training and optimization of CNN, the system can identify and analyze motion data more accurately, and improve the performance and effect of the motion capture system. The motion capture system based on CNN sensor can realize real-time monitoring and prevention of sports injuries for basketball players, provide more comprehensive, scientific and real-time sports data for athletes and coaches, help them better conduct training and adjust tactics, and improve the competitive level and safety of basketball games.