review · IEEE Access
Hand gestures serve as the primary communication mode for hearing-impaired individuals, yet bridging communication with non-hearing-impaired people remains a major global challenge. This systematic review analyses literature published between 2018 and 2023 across major scientific databases to assess vision, sensor, and hybrid hand gesture recognition methods. Evaluation focused on data acquisition, gesture representation, and recognition accuracy. The analysis reveals that signer-dependent systems achieve accuracy rates between 64% and 98%, averaging 87.9%. In contrast, signer-independent applications perform lower, ranging from 52% to 98% with an average accuracy of 79%. The review identifies continuous gesture identification and limited dataset sizes as ongoing technical obstacles that impede the practical viability of vision-based systems, offering recommendations to guide future research directions in sign language recognition.
Millions of hearing-impaired individuals face daily communication barriers when interacting with the wider public. By evaluating the performance and technical limits of current gesture recognition approaches, this work highlights the persistent performance gaps between specialised and universal signers. Addressing these gaps is crucial for creating dependable assistive technologies that enhance accessibility and improve social inclusion.
This work informs assistive technology developers building vision, sensor, or hybrid tools to translate sign language for hearing-impaired users. The technology remains at an academic, early research stage. Broad commercial adoption faces significant hurdles because system accuracy drops in real-world scenarios where the signer is unknown, and continuous gesture recognition remains technically difficult.
AI-generated from the published abstract. Always read the original work before citing.
Hand gesture is the main method of communication for people who are hearing-impaired, which poses a difficulty for millions of individuals worldwide when engaging with those who do not have hearing impairments. The significance of technology in enhancing accessibility and thereby increasing the quality of life for individuals with hearing impairments is universally recognized. Therefore, this study conducts a systematic review of existing literature review on hand gesture recognition, with a particular focus on existing methods that address the application of vision, sensor, and hybrid-based methods in the context of hand gesture recognition. This systematic review covers the period from 2018 to 2023, making use of prominent databases including IEEE Xplore, Science Direct, Scopus, and Web of Science. The chosen articles were carefully examined according to predetermined criteria for inclusion and disqualification. Our main focus was on evaluating the hand gesture representation, data acquisition, and accuracy of vision, sensor, and hybrid-based methods for recognizing hand gestures. The accuracy of discernment in scenarios that rely on the specific signer varies from 64% to 98%, with an average of 87.9% among the studies that were analyzed. On the other hand, in situations where the signer’s identity is not important, the accuracy of recognition ranges from 52% to 98%, with an average of 79% based on the research analyzed. The problems observed in continuous gesture identification highlight the need for more research efforts to improve the practical feasibility of vision-based gesture recognition systems. The findings also indicate that the size of the dataset continues to be a significant obstacle to hand gesture detection. Hence, this study seeks to provide a guide for future research by examining the academic motivations, challenges, and recommendations in the developing field of sign language recognition.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1109/access.2024.3421992
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
