2026-01-28 エディンバラ大学
The low-cost gloves detect hand gestures and subtle movements more accurately than existing technologies, researchers say.
＜関連情報＞
- https://www.ed.ac.uk/news/gloves-to-help-robots-get-to-grips-with-human-tasks
- https://www.research.ed.ac.uk/en/publications/modular-soft-wearable-glove-for-real-time-gesture-recognition-and/
リアルタイムのジェスチャー認識と動的3D形状再構成を実現するモジュラー型ソフトウェアラブルグローブ Modular Soft Wearable Glove for Real-Time Gesture Recognition and Dynamic 3D Shape Reconstruction
Huazhi Dong, Chunpeng Wang, Mingyuan Jiang, Francesco Giorgio-Serchi, Yunjie Yang
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems Duration: 19 Oct 2025
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±3.231 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 econstruction, making it a promising solution for next-generation HCI systems.
