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

Abstract

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.

A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence

“…Sign language data collection can be categorized into two methods: contact and non-contact. Contact equipment was previously used for gesture recognition, with typical examples including data gloves [116][117][118], myoelectric signal armbands, inertial measurement units (IMU) [119], WiFi [120], radar, smartphones, Leap Motion controllers [121], and Kinect [122,123], etc. The equipment can directly detect the spatial information of the human hand and each joint and process it into input data.…”

Anthropologically oriented strategies of interaction in the Human-Computer system

“…Interesting in this direction are the studies of recent years, which we will briefly present (figure 1, figure 2, figure 3). In the study by Saeed et al [4], having conducted an analysis of articles from 2017 to 2022 devoted to the problem of using gestures for human-computer interaction (HCI) using Sensory Gloves, comes to the conclusion that one of the directions for solving this problem is the improvement of the relevant deep learning algorithms and the decision to reduce the size of the equipment for greater compatibility and mobility of the system, as well as the search for compromises between robustness and sensitivity of the system. The system-organizing factor in this study was the dominance of technical solutions over anthropological solutions.…”

Experimental Evaluation of EMKEY: An Assistive Technology for People with Upper Limb Disabilities

“…Assistive technologies (ATs) are a research field that is transforming the lives of impaired people [1]. These technologies are intended to provide support and assistance to people with a variety of disabilities, including mobility [2,3], vision [4] and hearing impairments [5], and cognitive disabilities [6]. It includes a variety of hardware and software approaches that apply the latest advances in technology, such as: speech recognition [7,8], computer vision [9], brain-computer interfaces [10], eye tracking [11], head and face tracking [12][13][14][15], etc.…”