Classification of gestures of the colombian sign language from the analysis of electromyographic signals using artificial neural networks

Abstract

The objective of this article is to classify the 27 gestures of the Colombian sign alphabet, by means of a classifier of artificial neural networks based on electromyographic signals. The classifier was designed in four phases: Acquisition of electromyographic signals from the eight sensors of the Myo Armband handle, extraction of characteristics of the electromyographic signals using the wavelet transform of packages, training of the neural network and validation of the classification method using the cross-validation technique. For the present study, records of electromyographic signals from 13 subjects with hearing impairment were acquired. The classifier presented an average accuracy percentage of 88.4%, very similar to other classification methods presented in the literature. The classification method can be scaled to classify, in addition to the 27 gestures, the vocabulary of the Colombian sign language.