ArticleSensors (Basel, Switzerland)2020
Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Gesture recognition for hearing impaired people using an ensemble of deep learning models with improving beluga whale optimization-based hyperparameter tuning.Scientific reports · 2025Article
- MOT: A Low-Latency, Multichannel Wireless Surface Electromyography Acquisition System Based on the AD8232 Front-End.Sensors (Basel, Switzerland) · 2025Article
- Neuromorphic hardware for somatosensory neuroprostheses.Nature communications · 2024Review
- American Sign Language Recognition and Translation Using Perception Neuron Wearable Inertial Motion Capture System.Sensors (Basel, Switzerland) · 2024Article
- Intelligent sensors in assistive systems for deaf people: a comprehensive review.PeerJ. Computer science · 2024Article
- Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech.Sensors (Basel, Switzerland) · 2023Article
- American Sign Language Translation Using Wearable Inertial and Electromyography Sensors for Tracking Hand Movements and Facial Expressions.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sign languages are developed around the world for hearing-impaired people to communicate with others who understand them. Different grammar and alphabets limit the usage of sign languages between different sign language users. Furthermore, training is required for hearing-intact people to communicate with them. Therefore, in this paper, a real-time motion recognition system based on an electromyography signal is proposed for recognizing actual American Sign Language (ASL) hand motions for helping hearing-impaired people communicate with others and training normal people to understand the sign languages. A bilinear model is applied to deal with the on electromyography (EMG) data for decreasing the individual difference among different people. A long short-term memory neural network is used in this paper as the classifier. Twenty sign language motions in the ASL library are selected for recognition in order to increase the practicability of the system. The results indicate that this system can recognize these twenty motions with high accuracy among twenty participants. Therefore, this system has the potential to be widely applied to help hearing-impaired people for daily communication and normal people to understand the sign languages.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.