In this paper; we introduce an enhanced electromyography (EMG) pattern recognition algorithm based on a split-and-merge deep belief network (SM-DBN). Generally, it is difficult to classify the EMG features because the EMG signal has nonlinear and time-varying characteristics. Therefore, various machine-learning methods have been applied in several previously published studies. A DBN is a fast greedy learning algorithm that can identify a fairly good set of weights rapidly—even in deep networks with a large number of parameters and many hidden layers. To reduce overfitting and to enhance performance, the adopted optimization method was based on genetic algorithms (GA). As a result, the performance of the SM-DBN was 12.06% higher than conventional DBN. Additionally, SM-DBN results in a short convergence time, thereby reducing the training epoch. It is thus efficient in reducing the risk of overfitting. It is verified that the optimization was improved using GA.

Although physical exercise of patients is very important for their quality of life, the guide line for physical movement considering muscle fatigue is not insufficient. In this study, we propose an easy detection algorithm for muscle fatigue estimation. EMG signals of five healthy volunteers were measured at the quadriceps femoris muscle and the tibialis anterio in order to estimate muscle fatigue. RMMV (ratio between maximum and minimum value) of median frequency components of EMG is proposed as a muscle fatigue parameter. With our proposed parameter, muscle fatigue state was detected easily. It is expected that our method could be used for a parameter that recommend exercise intensity during gait rehabilitation.