Researchers have been developing Hand Gesture Recognition (HGR) systems to enhance natural, efficient, and authentic human-computer interaction, especially benefiting those who rely solely on hand gestures for communication. Despite significant progress, the automatic and precise identification of hand gestures remains a considerable challenge in computer vision. Recent studies have focused on specific modalities like RGB images, skeleton data, and spatiotemporal interest points. This paper provides a comprehensive review of HGR techniques and data modalities from 2014 to 2024, exploring advancements in sensor technology and computer vision. We highlight accomplishments using various modalities, including RGB, Skeleton, Depth, Audio, EMG, EEG, and Multimodal approaches and identify areas needing further research. We reviewed over 200 articles from prominent databases, focusing on data collection, data settings, and gesture representation. Our review assesses the efficacy of HGR systems through their recognition accuracy and identifies a gap in research on continuous gesture recognition, indicating the need for improved vision-based gesture systems. The field has experienced steady research progress, including advancements in hand-crafted features and deep learning (DL) techniques. Additionally, we report on the promising developments in HGR methods and the area of multimodal approaches. We hope this survey will serve as a potential guideline for diverse data modality-based HGR research.

本研究针对手势识别系统中自动精确识别手势的挑战，进行了2014至2024年的技术和数据模态全面综述，强调了传感器技术和计算机视觉的进展。我们发现了当前研究中对连续手势识别的关注不足，并呼吁改进基于视觉的手势系统，提出了多模态手势识别的前景和研究方向。

手势识别的多数据模态方法论与结构综述