An Intelligent Gesture Recognition Gloves for Real-time Monitoring in Wireless Human-Computer Interaction

Flexible sensors mimic the sensing ability of human skin, and have unique flexibility and adaptability, allowing users to interact with intelligent systems in a more natural and intimate way. In order to solve the problem of high sensitivity and wide operating range of flexible strain sensors, this study presents an innovative preparation method to develop a conductive elastomeric sensor with a cracked thin film by combining polydimethylsiloxane (PDMS) with multi-walled carbon nanotubes (MCNTs), which significantly improves the sensitivity and overcomes the limitation of the operating range (strain range 0-50%, GFmax= 4.97). And it has the advantages of simple preparation, low price and large-scale preparation. These sensors are affixed to the joints of the fingers and integrated with a dedicated hardware and software system to enable real-time monitoring of finger status and accurate gesture recognition, as well as the use of machine-learning algorithms to categorize American gestures with an accuracy of up to 97%. This innovation provides strong support for the future development of intelligent interactive systems.

柔性传感器可模拟人类皮肤的感知功能，兼具独特的柔性与环境适配性，能够让用户以更自然、更贴近的方式与智能系统进行交互。为解决柔性应变传感器高灵敏度与宽工作范围难以兼顾的技术难题，本研究提出一种创新制备方案：通过将聚二甲基硅氧烷（polydimethylsiloxane, PDMS）与多壁碳纳米管（multi-walled carbon nanotubes, MCNTs）相结合，制备出带有裂纹薄膜结构的导电弹性体传感器。该传感器不仅显著提升了灵敏度，还突破了工作范围的限制，其应变范围可达0~50%，最大灵敏因子GFmax=4.97。该传感器具备制备工艺简便、成本低廉且可规模化生产的优势。将此类传感器贴附于手指关节部位，并集成专用软硬件系统，即可实现手指运动状态的实时监测与精准手势识别；同时借助机器学习算法可完成美式手势分类，分类准确率最高可达97%。这一创新成果为未来智能交互系统的发展提供了坚实的技术支撑。