Abstract
According to the World Federation of Deaf [1], there are about 70 million deaf people who use sign language as their first language. Despite the fact that sign languages represent the main way of communication for the 1% of the world population, they are still used by a minority of hearing people. No one can ignore the obvious barrier of the communication between deaf and hearing people. In this context, our project contributes to improving the accessibility of the deaf. Indeed, this work is a contribution in a new field of sign language's recognition through EMG signals. This article presents the first step started by the research laboratory LaTICE (www.latice.rnu.tn) to evaluates the usage EMG electromyogram signals provided by the sensors of Myo armband, in order to facilitate the communication between hearing and deaf people, and therefore enrich the library of gestures recognized by this device. In this paper, we will be focusing on extracting characteristics of the raw electromyographic signals obtained from the Myo armband to classify some hand-shapes of the language's alphabet, based on a supervised automatic learning approach.
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