The Prediction of Ankle Injury Based on sEMG Using Cerebellar Model Neural Network

Abstract

Ankle joint injury is a kind of common and frequently-occurring clinical musculoskeletal injury disease. The clinical diagnosis of ankle joint injury is mostly based on conventional computed tomography (CT), X-ray and doctor's consultation. However, because of the insufficient understanding and cognition of ankle joint injury by some physicians, missed diagnosis and misdiagnosis are often caused. Therefore, the correct treatment measures and treatment time are always missed, making the acute ankle joint injury become chronic ankle instability. Surface electromyography (sEMG) is a non-stationary weak bioelectrical signal that is superimposed on the surface of the skin by the action of potential sequence generated by muscle excitability during exercise. SEMG has great performance on the recognition of human movements and the diagnosis of injuries because of its strong muscle specificity and differences in exercise patterns. In this paper, based on the difference of the information provided by sEMG measured by patients and healthy people, an intelligent diagnosis method is proposed to help doctors improve the accuracy of diagnosis. This intelligent diagnostic method is named cerebellar model neural network (CMNN) that imitates the learning mechanism of the human cerebellum. The trained CMNN can be seen as an expert who is good at ankle diagnosis and can help doctors make a better diagnose. This study reduces the number of iterations and speeds up the diagnosis by changing the loss function of CMNN from mean square error (MSE) to cross entropy.