Real-Time Hand Gesture Recognition in Human-Computer Interaction: A Comprehensive Review

Abstract

Real-Time Hand Gesture Recognition (HGR) has emerged as a key model of modern Human Computer Interaction (HCI) technologies, enabling touchless and intuitive interactive experiences for a wide range of HCI applications, from assistive technology, virtual and augmented reality to industrial production lines. Although AI and DL open up important opportunities, there are practical challenges to developing and deploying real-time HGR systems that are strong to issues such as computational complexity, variations in the environment, and latency. This survey reviews the state-of-the-art in real-time HGR, including recent work in and algorithmic approaches for various sensor modalities and different stages of the processing pipeline, including detection, tracking, and spatial-temporal classification, using monocular RGB cameras and multi-modal depth sensing systems. In addition, we cover applications such as sign language recognition, healthcare hygiene systems, automotive user interfaces, and human-robot collaboration (HRC), and challenges such as occlusion invariance, light variability, performance versus portability trade-offs, constraints imposed by edge-device deployment, and potential research avenues for multimodal artificial intelligence systems of the future.