Design of Sports Rehabilitation Training System Based on EEMD Algorithm

Abstract

Motor function rehabilitation training is to restore the motor function of hand injury to the maximum extent and meet the needs of patients’ daily behavior. At present, motor function evaluation and rehabilitation training work have disadvantages such as relying on the subjective experience of physicians, unable to quantitatively assess the loss of motor function, and single rehabilitation training method. Most of these methods only focus on the independent motion range of a single organ, lack of consideration of the constraint relationship between adjacent fingers, and do not build a visual model for it. To end this issue, for the purpose of sports rehabilitation, combined with the status and application of rehabilitation machines, this paper proposed a cycling rehabilitation training system based on physiological signal extraction of ensemble empirical mode decomposition (EEMD) algorithm. Results compared with the previous rehabilitation training, the muscle tension level of patients’ upper limbs decreased, and the strength of some muscles also increased. With the progress of rehabilitation training, the contralateral dominance coefficient showed an upward trend, which further confirmed the role of the proposed method in sports rehabilitation, and also provided a new idea for the evaluation of rehabilitation training effect of patients in the future.