Abstract
This paper presents the development and analysis of a system for Brazilian Sign Language (Libras) recognition, which is composed of an instrumented glove and an acquisition system. The instrumented glove has five flex sensors, an inertial sensor, and two contact sensors (made with silver-coated fabric). For data acquisition, five volunteers performed gestures in Libras. In the proposed segmentation technique, the collected data were segmented into three periods (fixed amounts of signal samples): construction period, alphabet gesture, and relaxation period. The periods of alphabet gesture are submitted to segmentation, and the classifier was designed to recognize the 26 letters of alphabet. Different classifiers were employed to classify the gestures and analyze the system in different scenarios. For the classification in group of sensors, the highest performance obtained was an accuracy of 96.15% with Random Forest (RF) classifier.
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