Adoption of bio-image technology on rehabilitation intervention of sports injury of golf

Abstract

This work aimed to explore the characteristics of surface electromyography (EMG) signal of golfers’ low back pain and the effect of rehabilitation. Based on the time-varying parameter autoregressive model and artificial neural network, ARAN algorithm was constructed, which was compared with the autoregressive moving average (ARMA) algorithm and the convolutional neural network (CNN) algorithm. Then, the established ARAN algorithm was employed to evaluate the characteristics of surface EMG signal of 106 golfers with low back pain. It was found that the accuracy, sensitivity, and specificity of the ARAN algorithm were superior to those of the CNN and ARMA algorithms. The golfer’s Roland-Morris Disability Questionnaire (RMDQ) score after treatment was less than that before treatment (P < 0.05). Moreover, there was significant negative correlation between RMDQ score and the mean values of time-varying parameters a1 and a3 (P < 0.05). The RMDQ score had a very obvious positive correlation with the mean values of a2, a4, and a6 (P < 0.001) and had a negative correlation with the mean value of a5 (P < 0.05). To sum up, the time-varying parameters of the surface EMG signal can effectively evaluate the golfer’s low back pain and the effect of treatment and rehabilitation.