Feature Optimization on Dual Leap Motion Controller for Indonesian Sign Language

Abstract

Sign language is a language formed by a combination of finger, hand, body movements and facial expressions used by persons with disabilities such as deaf and speech impaired. One of these sign language recognitions is recognition using Leap Motion Controller (LMC) sensor technology. In addition to the sign language that is formed has diversity such as folded fingers, hidden fingers, indonesian sign forms also have characteristics and shapes that are almost similar to one another. The LMC sensor is not always able to recognize all forms of signs properly. In this study, optimization is proposed at the feature level where optimization aims to provide more detailed features and characteristics of each sign language formed. The stages of the process are designing the layout of the sensors, adding features and combining feature data from each sensor. The test of the feature optimization on this dual LMC sensor can provide an increase in the recognition accuracy of the given Indonesian sign language. The Indonesian sign language can be recognized well with an average accuracy of 87.24% and the optimization carried out is able to produce an increase in accuracy of up to 2.88%.

AJAR UP 2021 as a basic introduction to sign language learning

“…wider. Generally, communication uses sign language in hand gestures or body cues, facial expressions, and other movements that symbolize Indonesian vocabulary (Bhakti et al, 2019;Nugraha et al, 2021).…”