Article
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Novel Wearable System to Recognize Sign Language in Real Time
Version 1
: Received: 29 May 2024 / Approved: 30 May 2024 / Online: 30 May 2024 (13:19:27 CEST)
A peer-reviewed article of this Preprint also exists.
Umut, İ.; Kumdereli, Ü.C. Novel Wearable System to Recognize Sign Language in Real Time. Sensors 2024, 24, 4613. Umut, İ.; Kumdereli, Ü.C. Novel Wearable System to Recognize Sign Language in Real Time. Sensors 2024, 24, 4613.
Abstract
The aim of this study is to develop a software solution for real-time recognition of sign language words using two arms. This will enable communication between hearing-impaired individuals and those who can hear. Several sign language recognition systems have been developed using different technologies, including cameras, armbands, and gloves. The system developed in this study utilizes surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. Other methods often have drawbacks, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have prevented their practical application. A software has been developed that can run on different operating systems using digital signal processing and machine learning methods specific to the study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The Random Forest algorithm was found to have the highest success rate with 99.875% accuracy, while the Naive Bayes algorithm had the lowest success rate with 87.625% accuracy. Feedback from a test group of 10 people indicated that the system is user-friendly, aesthetically appealing, and practically useful. The new system enables smoother communication for people with hearing disabilities and promises seamless integration into daily life without compromising user comfort or lifestyle quality.
Keywords
Artificial intelligence; Computer software; Human-computer interaction; Inertial measurement unit; Sign language recognition; Surface electromyography
Subject
Computer Science and Mathematics, Software
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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