Data-Glove for Japanese Sign Language Training System with Gyro-Sensor

Mori, Yuichiro; Toyonaga, Masahiko

doi:10.1109/scis-isis.2018.00211

Cited by 17 publications

(10 citation statements)

References 5 publications

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“…A data glove with gyro-sensor was introduced for Japanese sign language [8]. The glove was developed to have 5 sensors for the fingers and on the back of the palm, an accelerometer with gyro-sensor, was installed.…”

Section: Data Glove Methodsmentioning

confidence: 99%

Mask Region-Based Convolutional Neural Networks (R-CNN) for Sinhala Sign Language to Text Conversion

Sewwantha¹,

Ginige²

2021

Advances in Machine Learning, Data Mining and Computing

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Sign Language is the use of various gestures and symbols for communication. It is mainly used by disabled people with communication difficulties due to their speech or hearing impediments. Due to the lack of knowledge on sign language, natural language speakers like us, are not able to communicate with such people. As a result, a communication gap is created between sign language users and natural language speakers. It should also be noted that sign language differs from country to country. With American sign language being the most commonly used, in Sri Lanka, we use Sri Lankan/Sinhala sign language. In this research, the authors propose a mobile solution using a Region Based Convolutional Neural Network for object detection to reduce the communication gap between the sign users and language speakers by identifying and interpreting Sinhala sign language to Sinhala text using Natural Language Processing (NLP). The system is able to identify and interpret still gesture signs in real-time using the trained model. The proposed solution uses object detection for the identification of the signs.

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Section: Data Glove Methodsmentioning

confidence: 99%

Mask Region-Based Convolutional Neural Networks (R-CNN) for Sinhala Sign Language to Text Conversion

Sewwantha¹,

Ginige²

2021

Advances in Machine Learning, Data Mining and Computing

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“…Designing the device with minimal energy consumption using an electromagnetic harvesting system and using it only when needed is another motivation for hardware optimization [45]. The prototyping of the new glove-based system can be enhanced by connecting the mobile device to the glove, since mobile technology has become the personal carry-on machine of people, raising the likelihood of interaction by sign language users [46], [47] Thus, this will subsequently offer the window of opportunity to open more user-friendly mobile applications for sign language users [48]. Another motivation related to design enhancement is the need to find a translation technique for two-sided translation, which translates from text or speech to the sign language gestures and vice versa [49].…”

Section: ) Design Enhancementmentioning

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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“…Proposed using video and data gloves to recognize words according to hand, body shape, and finger bending, and binding specific sensors with data gloves to obtain the features of hand movement changes [6], the system recognizes 20 common Japanese gesture words with an average recognition accuracy of 51%. Although the system based on the wearable sensors can obtain fine-grained gesture features, it can not be widely used because of the problems of distance and portability.…”

Section: Related Workmentioning

confidence: 99%