The development of a wearable device for Sign Language Gesture Recognition (SLGR) by using Conceptual Design Approach (CDA)

In recent years, there has been a significant focus on researching and developing Sign Language Gesture Recognition (SLGR) for people with hearing and speaking impairments. This is especially important in Indonesia, where there are approximately two million people with these disabilities. However, current research and developments on SLGR devices are specifically designed to understand only particular sign language systems, such as Korean Standard Sign Language (KSDSL) and American Sign Language (ASL), each of which has its unique gestures and models. As a result, no device has been developed to recognize the gestures of the Indonesian sign language system, known as Sistem Isyarat Bahasa Indonesia (SIBI). Therefore, this study aims to develop an SLGR device that can recognize and translate SIBI gestures into output images via text and speech. The development of the SLGR device in this study is conducted by using the Conceptual Design Approach (CDA) methodology. Where in this case, previous research on SLGR devices is first observed as a benchmark for comparison. Furthermore, the benchmark is used as the basis for the function, sub-function, and specification of the proposed design. Based on these stages of benchmarking and concept development, the study concluded that the final design of the proposed SLGR device is constructed by using 5 flex and Gyroscope sensors connected wirelessly to the Raspberry microcontroller. The device is equipped with a voice system and LCR RPi as the output system for translation. Based on the combination of these sensors, the device is able to identify any particular gestures that correspond to words and phrases in SIBI and translate them into speech via the designated speaker and text display on the LCD screen. To fully understand the performance of the device, experimental tests are conducted by analyzing the input of 26 alphabets in the SIBI system. As a result, the device demonstrated an average of 92% accuracy to convert sign language into voice and text, which demonstrates the usefulness of the proposed device