Virtual Assistance Environment for Deaf People Based on an Electronic Gauntlet

Guzman, Diego Enrique; Brito, Geovanni; Naranjo, José E.; García, Carlos A.; Saltos, Lenin F.; García, Marcelo V.

doi:10.1109/etcm.2018.8580281

Cited by 7 publications

(1 citation statement)

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“…Firstly, for the sensors-related recommendations, there is an obvious need for more sensors to be added so that all the fingers are covered to increase the accuracy of the system [19], [78] ,and [79]. Undeniably, another vital factor with regards to sensors is the question of the type of sensors that is to be used, such as using gyroscope sensor [80], accelerometer sensor [81], and force sensors which will allow the detection of even more complex gestures and hand positions as the user interacts with the device [59]. Secondly, aside from sensors, another category of recommendations by the researchers was more associated with experiments, and its factors such as using operations like addition and more advanced analysis like machine learning which has been stated in the work of [62], [72], [82].…”

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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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