Evaluating Sign Language Recognition Using the Myo Armband

Abstract

The successful recognition of sign language gestures by computer systems would greatly improve communications between the deaf and the hearers. This work evaluates the usage of electromyogram (EMG) data provided by the Myo armband as features for classification of 20 stationary letter gestures from the Brazilian Sign Language (LIBRAS) alphabet. The classification was performed by binary Support Vector Machines (SVMs), trained with a one-vs-all strategy. The results obtained show that it is possible to identify the gestures, but substantial limitations were found that would need to be tackled by further studies.