Abstract
Dynamic hand gestures play a pivotal role in assistive human-robot interaction (HRI), facilitating intuitive, non-verbal communication, particularly for individuals with mobility constraints or those operating robots remotely. Current gesture recognition methods are mostly limited to short-range interactions, reducing their utility in scenarios demanding robust assistive communication from afar. In this paper, we introduce a novel approach designed specifically for assistive robotics, enabling dynamic gesture recognition at extended distances of up to 30 meters, thereby significantly improving accessibility and quality of life. Our proposed Distance-aware Gesture Network (DiG-Net) effectively combines Depth-Conditioned Deformable Alignment (DADA) blocks with Spatio-Temporal Graph modules, enabling robust processing and classification of gesture sequences captured under challenging conditions, including significant physical attenuation, reduced resolution, and dynamic gesture variations commonly experienced in real-world assistive environments. We further introduce the Radiometric Spatio-Temporal Depth Attenuation Loss (RSTDAL), shown to enhance learning and strengthen model robustness across varying distances. Our model demonstrates significant performance improvement over state-of-the-art gesture recognition frameworks, achieving a recognition accuracy of 97.3% on a diverse dataset with challenging hyper-range gestures. By effectively interpreting gestures from considerable distances, DiG-Net significantly enhances the usability of assistive robots in home healthcare, industrial safety, and remote assistance scenarios, enabling seamless and intuitive interactions for users regardless of physical limitations
Abstract (translated)
动态手势在辅助人机交互（HRI）中扮演着关键角色，它促进了直观的非语言交流，特别是对于行动受限或远程操作机器人的个人而言。目前的手势识别方法大多局限于短距离互动，这限制了它们在需要远距离可靠辅助通信场景中的实用性。本文介绍了一种专为辅助机器人设计的新方法，能够在长达30米的距离内实现动态手势识别，从而显著提高可访问性和生活质量。 我们提出的距离感知手势网络（DiG-Net）有效地结合了深度条件形变对齐（DADA）模块和时空图模块，能够处理在实际辅助环境中常见的挑战性情况下的手势序列的稳健处理与分类。这些挑战包括物理衰减、分辨率降低以及动态的手势变化等。我们还引入了辐射时空深度衰减损失（RSTDAL），以增强模型的学习能力和跨不同距离的鲁棒性。 我们的模型在包含具有极高难度远距手势的数据集上，相比最先进的手势识别框架取得了显著的性能提升，达到了97.3%的识别准确率。通过有效地从较远的距离解释手势，DiG-Net极大地提升了辅助机器人在家用医疗保健、工业安全和远程援助场景中的可用性，使用户能够不受物理限制地实现无缝直观互动。 这种技术进步不仅提高了机器人的用户体验，还为需要特殊帮助的人群提供了更多的独立性和灵活性。通过改善人机交互的方式，DiG-Net有助于推动智能辅助设备在日常生活中的广泛应用和发展。
URL
https://arxiv.org/abs/2505.24786
