Artificial Intelligence Algorithms in Sports Rehabilitation Control Management System

The aim of this study is to explore and propose the application of artificial intelligence algorithms in sports rehabilitation control management systems, in order to solve the problems existing in traditional research. By introducing advanced artificial intelligence technologies, including machine learning, deep learning, data mining, personalized rehabilitation plans can be achieved, and data analysis efficiency can be improved. Real-time monitoring and feedback can also be achieved, and intelligent decision support can be provided to promote more intelligent and personalized rehabilitation treatment, thus improving the quality of life and rehabilitation outcomes of patients. The performances of long short-term memory (LSTM) and traditional recurrent neural network (RNN) in terms of rehabilitation action recognition accuracy are compared through experiments. The experimental results show that the average accuracy of LSTM reaches 75%, significantly higher than the 60% of traditional RNN. In terms of recall ratio for rehabilitation actions, LSTM achieves 84%, while traditional RNN only achieves 69%. These clear numerical differences clearly demonstrate the excellent performance of LSTM in rehabilitation action recognition. This study provides empirical support for the application of artificial intelligence algorithms in sports rehabilitation, revealing the enormous potential of LSTM in the field of rehabilitation.