Sensor fusion of motion-based sign language interpretation with deep learning

Lee, Boon Giin and Chong, Teak-Wei and Chung, Wan-Young (2020) Sensor fusion of motion-based sign language interpretation with deep learning. Sensors, 20 (21). p. 6256. ISSN 1424-8220

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Abstract

Sign language was designed to allow hearing-impaired people to interact with others. Nonetheless, knowledge of sign language is uncommon in society, which leads to a communication barrier with the hearing-impaired community. Many studies of sign language recognition utilizing computer vision (CV) have been conducted worldwide to reduce such barriers. However, this approach is restricted by the visual angle and highly affected by environmental factors. In addition, CV usually involves the use of machine learning, which requires collaboration of a team of experts and utilization of high-cost hardware utilities; this increases the application cost in real-world situations. Thus, this study aims to design and implement a smart wearable American Sign Language (ASL) interpretation system using deep learning, which applies sensor fusion that “fuses” six inertial measurement units (IMUs). The IMUs are attached to all fingertips and the back of the hand to recognize sign language gestures; thus, the proposed method is not restricted by the field of view. The study reveals that this model achieves an average recognition rate of 99.81% for dynamic ASL gestures. Moreover, the proposed ASL recognition system can be further integrated with ICT and IoT technology to provide a feasible solution to assist hearing-impaired people in communicating with others and improve their quality of life.

Item Type: Article
Keywords: deep learning; human-computer interaction; motion sensor; sensor fusion; sign language recognition; wearable computing
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number: https://doi.org/10.3390/s20216256
Depositing User: Yu, Tiffany
Date Deposited: 18 Nov 2020 01:18
Last Modified: 18 Nov 2020 01:18
URI: http://eprints.nottingham.ac.uk/id/eprint/63821

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