Talking Gloves: Low-Cost Gesture Recognition System for Sign Language Translation

Shaheen, Hina; Mehmood, Tariq

doi:10.1109/tenconspring.2018.8692069

Cited by 12 publications

(5 citation statements)

References 5 publications

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“…The system can go into its resting mode and save energy when no sign is entered. The system armature avoids complicated calculations by using a straightforward system that directly maps movements via a lookup procedure [7]. This paper will enable silent persons to fete colourful hand gestures while wearing gloves, and these gestures will be restated into applicable addresses for non-silent people.…”

Section: Related Workmentioning

confidence: 99%

Talking Hands & D and D (Dump & Deaf) Assistant

2024

IJFMR

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The largest issue facing the deaf and dumb community these days is communication with both the general public and fellow deaf and dumb people. Ordinary individuals can learn new information from spoken language, everyday conversations, and surrounding sounds. Individuals who are hard of hearing or deaf do not enjoy this luxury. These people will gain from this approach since it provides a means of communication. The term "assistive technology" describes gadgets that are rehabilitative, adaptive, and made for people with disabilities. This project aims to develop a system that can recognize sign language, bridging the communication gap by enabling those with speech impairments to interact with others who do not. Hand gestures are important because, in contrast to other techniques, they enable people to express their ideas rapidly. The current work aims to recognize signs using an image processing-based gesture recognition module and convert the corresponding speech to text using a text-to-speech synthesizer

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Section: Related Workmentioning

confidence: 99%

Talking Hands & D and D (Dump & Deaf) Assistant

2024

IJFMR

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“…Based on the recommendations of many researchers, additional progress may be achieved by expanding the database set, [143] so that the hearing-impaired population can easily interact with others using an extensive vocabulary of Sign Language. [144][145][146][147][148][149]…”

Section: Human-machine Interfacementioning

confidence: 99%

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

Tchantchane,

Zhou,

Zhang

et al. 2023

Advanced Intelligent Systems

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Hand gesture, one of the essential ways for a human to convey information and express intuitive intention, has a significant degree of differentiation, substantial flexibility, and high robustness of information transmission to make hand gesture recognition (HGR) one of the research hotspots in the fields of human–human and human–computer or human–machine interactions. Noninvasive, on‐body sensors can monitor, track, and recognize hand gestures for various applications such as sign language recognition, rehabilitation, myoelectric control for prosthetic hands and human–machine interface (HMI), and many other applications. This article systematically reviews recent achievements from noninvasive upper‐limb sensing techniques for HGR, multimodal sensing fusion to gain additional user information, and wearable gesture recognition algorithms to obtain more reliable and robust performance. Research challenges, progress, and emerging opportunities for sensor‐based HGR systems are also analyzed to provide perspectives for future research and progress.

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“…At the researchers' recommendation, it was clear that data in itself is another important factor, and researchers in the literature have agreed on its importance to involve fluent users of American sign language (ASL) in a more natural setting . [ 91 ] Based on the recommendations of a large number of researchers, additional progress may be achieved by expanding the database set [16], so that the Deaf population can easily interact with others using an extensive vocabulary of Sign Language [31], [92].…”

Section: ) Recommendations Related To Researchersmentioning

confidence: 99%

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

et al. 2022

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Sign language is the predominant mode of communication for the Deaf community. For the millions of people who suffer from hearing loss around the world, interaction with people who have the ability to hear and do not suffer from hearing impairment or loss is considered as complicated. In line with this issue, technology is perceived as a crucial factor in being an enabler of providing solutions to enhance the quality of life of the Deaf and people with hearing impairment by increasing accessibility. This research aims to review and analyze articles related to sign language recognition based on the sensor-based glove system, in order to identify academic motivations, challenges, and recommendations related to this field. The search for the relevant review materials and articles was performed on four major databases ranging from 2017 to 2022: Science Direct, Web of Science, IEEE Xplore, and Scopus. The articles were chosen based on our inclusion and exclusion criteria. The literature findings of this paper indicate the dataset size to be open issues and challenges for hand gesture recognition. Furthermore, the majority of research on sign language recognition based on data glove was performed on static, single hand, and isolated gestures. Moreover, recognition accuracy typically achieved results higher than 90%. However, most experiments were carried out with a limited number of gestures. Overall, it is hoped that this study will serve as a roadmap for future research and raise awareness among researchers in the field of sign language recognition.

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Talking Gloves: Low-Cost Gesture Recognition System for Sign Language Translation

Cited by 12 publications

References 5 publications

Talking Hands & D and D (Dump & Deaf) Assistant

Talking Hands & D and D (Dump & Deaf) Assistant

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

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