Abstract
With the increasing demand for human-computer interaction (HCI), flexible wearable gloves have emerged as a promising solution in virtual reality, medical rehabilitation, and industrial automation. However, the current technology still has problems like insufficient sensitivity and limited durability, which hinder its wide application. This paper presents a highly sensitive, modular, and flexible capacitive sensor based on line-shaped electrodes and liquid metal (EGaIn), integrated into a sensor module tailored to the human hand's anatomy. The proposed system independently captures bending information from each finger joint, while additional measurements between adjacent fingers enable the recording of subtle variations in inter-finger spacing. This design enables accurate gesture recognition and dynamic hand morphological reconstruction of complex movements using point clouds. Experimental results demonstrate that our classifier based on Convolution Neural Network (CNN) and Multilayer Perceptron (MLP) achieves an accuracy of 99.15% across 30 gestures. Meanwhile, a transformer-based Deep Neural Network (DNN) accurately reconstructs dynamic hand shapes with an Average Distance (AD) of 2.076\pm3.231 mm, with the reconstruction accuracy at individual key points surpassing SOTA benchmarks by 9.7% to 64.9%. The proposed glove shows excellent accuracy, robustness and scalability in gesture recognition and hand reconstruction, making it a promising solution for next-generation HCI systems.
Abstract (translated)
随着人机交互（HCI）需求的增长，柔性穿戴手套在虚拟现实、医疗康复和工业自动化等领域展现出巨大的潜力。然而，当前技术仍面临灵敏度不足和耐用性有限等问题，这些限制了其广泛应用。本文提出了一种基于线状电极和液态金属（EGaIn）的高度敏感、模块化且柔性的电容式传感器，并将其集成到一个符合人体手部解剖学特性的传感模块中。该系统能够独立捕捉每个指关节的弯曲信息，同时相邻手指之间的额外测量则允许记录指尖间距的细微变化。这一设计使得基于点云数据实现精确的手势识别和复杂运动下的动态手部形态重建成为可能。 实验结果表明，基于卷积神经网络（CNN）和多层感知机（MLP）的分类器在30种手势中达到了99.15%的准确率。同时，一种基于变压器的深度神经网络能够以平均距离2.076±3.231毫米的高度准确性重构动态手形，并且其关键点重建精度超过现有最佳方法（SOTA）基准9.7%到64.9%。 所提出的智能手套在手势识别和手部形态重建方面表现出色，具有极高的准确度、鲁棒性和可扩展性，为下一代人机交互系统提供了一个有前景的解决方案。
URL
https://arxiv.org/abs/2504.05983
