SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine

This paper proposes a scheme consisting of two novel components to recognize multiple hand motions from surface electromyography (SEMG). First, we use the cumulative residual entropy (CREn), a measure of uncertainty in a random variable, as the feature. Second, we employ the extreme learning machine (ELM), a fast and effective classifier using single-hidden layer feedforward neural network with additive neurons, to distinguish different motions. To evaluate performance of the proposed system, we compare CREn with fuzzy entropy, sample entropy, and approximate entropy, and a state-of-the-art time-domain feature; and ELM with linear discriminant analysis and support vector machine. They are tested on four channel SEMG signals acquired from ten normal subjects. Experimental results indicate that the classification accuracies of CREn are not only better than those of other entropies with all the classifiers, but also comparable to the time-domain feature for all the segment lengths of 200, 250 and 1,000 ms with all classifiers that are evaluated. Furthermore, the computational complexity of CREn is lower than those of other features, and ELM performs significantly faster than other classifiers without sacrificing any performance. It suggests that the proposed CREn-ELM scheme has the potential to be applied to real-time control of SEMG-based multifunctional prosthesis.