Prevention and Detection Research of Intelligent Sports Rehabilitation under the Background of Artificial Intelligence

Abstract

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.