Cheng Aobo, Li Xin, Li Ding, Chen Zhikang, Cui Tianrui, Tao Lu-Qi, Jian Jinming, Xiao HuiJun, Shao Wancheng, Tang Zeyi, Li Xinyue, Dong Zirui, Liu Houfang, Yang Yi, Ren Tian-Ling
School of Integrated Circuit, Tsinghua University, Beijing, P.R. China.
Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P.R. China.
Nat Commun. 2025 Jan 11;16(1):591. doi: 10.1038/s41467-024-55649-1.
Human-machine interaction has emerged as a revolutionary and transformative technology, bridging the gap between human and machine. Gesture recognition, capitalizing on the inherent dexterity of human hands, plays a crucial role in human-machine interaction. However, existing systems often struggle to meet user expectations in terms of comfort, wearability, and seamless daily integration. Here, we propose a handwriting recognition technology utilizing an intelligent hybrid-fabric wristband system. This system integrates spun-film sensors into textiles to form the smart fabric, enabling intelligent functionalities. A thermal encapsulation process is proposed to bond multiple spun-films without additional materials, ensuring the lightweight, breathability, and stretchability of the spun-film sensors. Furthermore, recognition algorithms facilitate precise accurate handwriting recognition of letters, with an accuracy of 96.63%. This system represents a significant step forward in the development of ergonomic and user-friendly wearable devices for enhanced human-machine interaction, particularly in the virtual world.
人机交互已成为一项具有革命性和变革性的技术,弥合了人与机器之间的差距。手势识别利用人类手部固有的灵活性,在人机交互中发挥着关键作用。然而,现有系统在舒适性、可穿戴性和日常无缝集成方面往往难以满足用户期望。在此,我们提出一种利用智能混合织物腕带系统的手写识别技术。该系统将纺丝薄膜传感器集成到纺织品中以形成智能织物,实现智能功能。提出了一种热封装工艺,无需额外材料即可粘结多个纺丝薄膜,确保纺丝薄膜传感器的轻便、透气和可拉伸性。此外,识别算法有助于精确准确地识别字母笔迹,准确率达96.63%。该系统在开发用于增强人机交互的人体工程学和用户友好型可穿戴设备方面迈出了重要一步,尤其是在虚拟世界中。