Real Time Motion Monitoring System based on Deep Learning Algorithm

In order to solve the problems of accuracy, real-time performance and lack of personalized guidance in the field of real-time sports monitoring, this paper introduces CNN-LSTM to design and implement an efficient real-time sports monitoring system. In the data acquisition layer, this paper uses wearable devices to capture the physiological data of athletes in real time, and pre-processes the raw data in the data processing layer. The model analysis layer combines convolutional neural network (CNN) and long short-term memory network (LSTM). In the application service layer, the system provides sports guidance to athletes based on the results of model analysis. The proposed system is superior to traditional systems in terms of state recognition accuracy and real-time performance, with an average recognition delay of only 133.24 milliseconds. The proposed system provides a new technical solution for the field of sports monitoring and shows its advantages.