Classification of myoelectric surface signals of hand movements using supervised learning techniques

This work presents a comparative study of techniques to classify four hand movements (flexion, extension, opening and closure) using myoelectric signals measured at the forearm in two separate channels: the brachioradialis and the flexor carpi ulnaris (FCU) muscle. The process of signal acquisition is described, as well as signal normalization, hybrid feature extraction and classification using two supervised learning techniques; i.e., backpropagation and support vector machines. The classifiers were trained using the raw data from the input signal. It was verified that the accuracy of the classification is improved by feature extraction up to 2.25%, yielding a successful average classification rate of 91.00%.