Recognising Irish Sign Language Using Electromyography

Galea, Laura Cristina; Smeaton, Alan F.

doi:10.1109/cbmi.2019.8877421

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…The results indicated that avatar-based tutoring was more effective in assessing a child's knowledge of certain words in sign language. In the field of education, dictionaries in English [34], Irish [48], Arabic [32] and Indian [37], [38] as well as Portuguese [42] dictionaries may also be considered.…”

Section: A Education Applicationmentioning

confidence: 99%

Sign language interpreting - relationships between research in different areas - overview

Probierz,

Kozak,

Piasecki

et al. 2023

Annals of Computer Science and Information Systems

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Translation from the national language into sign language is an extremely important area of research and practice, which aims to ensure communication between deaf or hard of hearing people and the hearing community. The article provides an overview of the most important research on sign language interpretation conducted in various research areas. The latest scientific and theoretical achievements were presented, which contribute to a better understanding of the subject of sign language translation and the improvement of the quality of translation services. Our main goal is to identify outstanding areas of interdisciplinary research related to sign language translation and to identify links between these studies conducted in different areas. The conclusions of the article aim to broaden the knowledge and awareness of sign language translation and to identify areas that require further research and development. The work is linked to a project related to the application of machine learning in increasing accessibility for deaf people.

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Section: A Education Applicationmentioning

confidence: 99%

Sign language interpreting - relationships between research in different areas - overview

Probierz,

Kozak,

Piasecki

et al. 2023

Annals of Computer Science and Information Systems

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show abstract

“…On the other hand, wearable sensors have been adopted for capturing sign language gestures. Galea et al in [ 20 ], used electromyography (EMG) to capture electrical activity that was produced during arm movement. The Thalmic MYO armband device was used for the recording of Irish sign language alphabet.…”

Section: Sign Language Capturingmentioning

confidence: 99%

Artificial Intelligence Technologies for Sign Language

Papastratis

Chatzikonstantinou

Konstantinidis

et al. 2021

Sensors

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AI technologies can play an important role in breaking down the communication barriers of deaf or hearing-impaired people with other communities, contributing significantly to their social inclusion. Recent advances in both sensing technologies and AI algorithms have paved the way for the development of various applications aiming at fulfilling the needs of deaf and hearing-impaired communities. To this end, this survey aims to provide a comprehensive review of state-of-the-art methods in sign language capturing, recognition, translation and representation, pinpointing their advantages and limitations. In addition, the survey presents a number of applications, while it discusses the main challenges in the field of sign language technologies. Future research direction are also proposed in order to assist prospective researchers towards further advancing the field.

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“…As a meaning to construct individual communication, sign languages do not share the same standard worldwide. Instead, cultural difference along with other factors create a huge difference among the sign languages [ 3 , 4 , 5 , 6 ]. The physical component of sign languages usually consists of movement of forearm and hand motion.…”

Section: Introductionmentioning

confidence: 99%

Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal

Tateno

Liu

2020

Sensors

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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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Recognising Irish Sign Language Using Electromyography

Cited by 7 publications

References 7 publications

Sign language interpreting - relationships between research in different areas - overview

Sign language interpreting - relationships between research in different areas - overview

Artificial Intelligence Technologies for Sign Language

Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal

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