A Cost Effective Design and Implementation of Arduino Based Sign Language Interpreter

Sengupta, Anirbit; Mallick, Tausif; Das, Abhijit

doi:10.1109/devic.2019.8783574

Cited by 16 publications

(8 citation statements)

References 2 publications

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“…SLR can read and process the hand movements which could pave a path for barrier-free communication. [131] Most researchers have concentrated on a small number of gestures such as some alphabets [132,133] and some numbers, [134,135] a few word gestures, [136,137] or a combination of the alphabets, numbers, and words. [138,139] Each country has its sign language, such as Australian Sign Language (ASL) and Arabic Sign Language (GSL), which makes it challenging to develop a standardized sign language detection system for worldwide use.…”

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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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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“…Glove-based techniques provide efficient and more feasible recognition rates comparative to vision-based techniques, especially in dark environments [26]. The lowand middle-income countries are the most affected by hearing impairment problems due to ear infections according to World Health Organization (WHO) reports [27]. This had been the impetus towards the motivation to find a low-cost, affordable device, that can translate the sign language into an oral language, to enable independent communication and movement for the speech/hearing impaired people in society [20], [28], [29].…”

Section: ) Benefits Of Using a Glove-based Systemmentioning

confidence: 99%

“…Firstly, in terms of new technology, it is clear that many recommendations are dedicated to using a wireless connection technology through the employment of Wi-Fi technology [ 27 ] , Bluetooth technology [ 56 ] between devices, and the use of a microcontroller to solve the problem of cables that can disturb the hand movements [8], [ 101 ] . On the other hand, incorporating a processing unit together with Wi-Fi connectivity will give the system the ability to use online free translation engines and increase the speed and accuracy of the resulting words, sentences, and phrases .…”

Section: ) Recommendations Related To Developersmentioning

confidence: 99%

“…The majority of the papers reviewed were focused on each selected author's data set. The vast majority of studies have focused on a small number of gestures such as some alphabets [27], [31], [107] [66], [34] , [108], [107] and some numbers [65], [51], few words gestures [8], [47], [101], [89] or a combination of the alphabets, numbers, and words [109], [82]. Figure 8 indicated the American Sign Language (ASL) alphabets and numbers [57].…”

Section: Datasetmentioning

confidence: 99%

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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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“…On the contrary, as it does not suffer from any of the above weaknesses related to computer vision, gesture-based SLR has gradually become one of the most promising approaches in SLR, in which sensor-intensive gloves [17][18][19], clothing [20], and watches [21][22][23] are used to record the movements of the signers. Among these devices, smart watches are one of the most popular choices for SLR, as they are not only available from the market, but also minimize intrusiveness to the signers.…”

Section: Introductionmentioning

confidence: 99%

A Portable Sign Language Collection and Translation Platform with Smart Watches Using a BLSTM-Based Multi-Feature Framework

2022

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Continuous sign language recognition (CSLR) using different types of sensors to precisely recognize sign language in real time is a very challenging but important research direction in sensor technology. Many previous methods are vision-based, with computationally intensive algorithms to process a large number of image/video frames possibly contaminated with noises, which can result in a large translation delay. On the other hand, gesture-based CSLR relying on hand movement data captured on wearable devices may require less computation resources and translation time. Thus, it is more efficient to provide instant translation during real-world communication. However, the insufficient amount of information provided by the wearable sensors often affect the overall performance of this system. To tackle this issue, we propose a bidirectional long short-term memory (BLSTM)-based multi-feature framework for conducting gesture-based CSLR precisely with two smart watches. In this framework, multiple sets of input features are extracted from the collected gesture data to provide a diverse spectrum of valuable information to the underlying BLSTM model for CSLR. To demonstrate the effectiveness of the proposed framework, we test it on an extremely challenging and radically new dataset of Hong Kong sign language (HKSL), in which hand movement data are collected from 6 individual signers for 50 different sentences. The experimental results reveal that the proposed framework attains a much lower word error rate compared with other existing machine learning or deep learning approaches for gesture-based CSLR. Based on this framework, we further propose a portable sign language collection and translation platform, which can simplify the procedure of collecting gesture-based sign language dataset and recognize sign language through smart watch data in real time, in order to break the communication barrier for the sign language users.

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A Cost Effective Design and Implementation of Arduino Based Sign Language Interpreter

Cited by 16 publications

References 2 publications

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

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

A Portable Sign Language Collection and Translation Platform with Smart Watches Using a BLSTM-Based Multi-Feature Framework

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