A Systematic Review of Hand Gesture Recognition: An Update From 2018 to 2024

ABSTRACT 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. INDEX TERMS Sign language recognition, dynamic hand gesture recognition, vision-based hand gesture,
sensor-based hand gesture, hybrid-based hand gesture, classification, feature extraction