LAPSE:2023.35143
Published Article
LAPSE:2023.35143
Application of Wearable Gloves for Assisted Learning of Sign Language Using Artificial Neural Networks
Hyeon-Jun Kim, Soo-Whang Baek
April 28, 2023
This study proposes the design and application of wearable gloves that can recognize sign language expressions from input images via long short-term memory (LSTM) network models and can learn sign language through finger movement generation and vibration motor feedback. It is difficult for nondisabled people who do not know sign language to express sign language accurately. Therefore, we suggest the use of wearable gloves for sign language education to help nondisabled people learn and accurately express sign language. The wearable glove consists of a direct current motor, a link (finger exoskeleton) that can generate finger movements, and a flexible sensor that recognizes the degree of finger bending. When the coordinates of the hand move in the input image, the sign language motion is fed back through the vibration motor attached to the wrist. The proposed wearable glove can learn 20 Korean sign language words, and the data used for learning are configured to represent the joint coordinates and joint angles of both the hands and body for these 20 sign language words. Prototypes were produced based on the design, and it was confirmed that the angle of each finger could be adjusted. Through experiments, a sign language recognition model was selected, and the validity of the proposed method was confirmed by comparing the generated learning results with the data sequence. Finally, we compared and verified the accuracy and learning loss using a recurrent neural network and confirmed that the test results of the LSTM model showed an accuracy of 85%.
Keywords
Artificial Intelligence, internet of things, LSTM, neural network, RNN, wearable
Suggested Citation
Kim HJ, Baek SW. Application of Wearable Gloves for Assisted Learning of Sign Language Using Artificial Neural Networks. (2023). LAPSE:2023.35143
Author Affiliations
Kim HJ: Department of Electronic Information System Engineering, Sangmyung University, Cheonan 31066, Republic of Korea
Baek SW: Department of Human Intelligence and Robot Engineering, Sangmyung University, Cheonan 31066, Republic of Korea [ORCID]
Journal Name
Processes
Volume
11
Issue
4
First Page
1065
Year
2023
Publication Date
2023-04-01
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr11041065, Publication Type: Journal Article
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LAPSE:2023.35143
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doi:10.3390/pr11041065
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Apr 28, 2023
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