Multi-channel electromyography pattern classification using deep belief networks for enhanced user experience

An enhanced algorithm is proposed to recognize multi-channel electromyography (EMG) patterns using deep belief networks (DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-varying characteristics. Therefore, in several previous studies, various machine-learning methods have been applied. A DBN is a fast, greedy learning algorithm that can find a fairly good set of weights rapidly, even in deep networks with a large number of parameters and many hidden layers. To evaluate this model, we acquired EMG signals, extracted their features, and then compared the model with the DBN and other conventional classifiers. The accuracy of the DBN is higher than that of the other algorithms. The classification performance of the DBN model designed is approximately 88.60%. It is 7.55% (p=9.82x10(-12)) higher than linear discriminant analysis (LDA) and 2.89% (p=1.94x10(-5)) higher than support vector machine (SVM). Further, the DBN is better than shallow learning algorithms or back propagation (BP), and this model is effective for an EMG-based user-interfaced system.