Sports rehabilitation training is a comprehensive discipline that solves and helps motor function recovery through physical exercise, also known as rehabilitation sports training. Sports rehabilitation training can help athletes restore health, treat injuries, prevent recurrence, improve physical fitness, and meet the needs of daily training. Starting from the concept of sports rehabilitation training, this paper analyzes the application of sports rehabilitation training in physical training and summarizes and analyzes the role of sports rehabilitation training. It provides reference for the application of physical rehabilitation training in sports training. Intelligent health monitoring technology has been widely used and developed, and many classic algorithms have emerged to complete the extraction of moving targets. This paper is aimed at studying the application of intelligent health monitoring to the construction of sports rehabilitation training models. It proposes the overall design of the intelligent health monitoring terminal and the common algorithms for moving target monitoring, such as optical flow method, frame difference method, and background difference method. It takes the rehabilitation training of an athlete in X city as an example to establish a model study. The final result of the experiment through sports rehabilitation training under intelligent health monitoring is that 92.6% of the athletes recovered from sports injuries through rehabilitation physical training.

Artificial intelligence can bring convenience to human life. In the field of sports rehabilitation, the application of artificial intelligence is becoming more and more in-depth. This paper is aimed at studying the prevention and detection of sports rehabilitation in the context of artificial intelligence and proposing a compliance control method for lower limb rehabilitation robots based on artificial neural networks. In this paper, a double closed-loop control system is designed: the outer loop is an adaptive impedance control model based on sEMG feedback, and the purpose is to adjust the predicted desired joint trajectories. In the inner loop, a sliding mode iterative learning controller is designed to suppress periodic disturbance and abnormal jitter and achieve stable tracking of the target trajectory. Finally, the control method is simulated and verified by matlab/simulink, and a statistical experiment is done on the patient’s recovery. The experimental results show that the use of artificial intelligence technology can effectively increase the sensitivity of the control system and improve the recovery rate of patients. Compared with the traditional sports rehabilitation control system, the sensitivity is increased by 22.7%, and the patient recovery rate is increased by 10.4%, which is of great significance in the field of sports rehabilitation.