RESUMO
Surface electromyography has become one of the popular methods for recognizing hand gestures. In this paper, the performance of four classification methods on sEMG signals have been investigated. These methods are developed by combinations of two feature extraction methods, including Mean Absolute Value and Short-Time Fourier Transform, and two classifiers, including Support Vector Machine and Convolutional Neural Network. These classification methods achieved an accuracy over 97 % on the NinaPro dataset 1. In addition, a new dataset, which includes the Activities of Daily Living, was proposed and an accuracy over 98 % was obtained by applying the presented classification methods.This methodology can provide the basis for a robust quantitative technique to evaluate hand grasps of stroke patients in performing activities of daily living that in turn can lead to a more efficient rehabilitation regimen.