Lee Kok-Tong, Lim Eng-Hock, Tan Chun-Hui, Low Jen-Hahn, Wong Kwong-Long, Guan Cao, Chee Pei-Song
Lee Kong Chian Faculty of Engineering and Science (LKC FES), Universiti Tunku Abdul Rahman (UTAR), Selangor 43000, Malaysia.
Center of Healthcare Science and Technology, Universiti Tunku Abdul Rahman (UTAR), Selangor 43000, Malaysia.
ACS Appl Mater Interfaces. 2024 Nov 13;16(45):62914-62924. doi: 10.1021/acsami.4c12371. Epub 2024 Nov 4.
The integration of flexible sensors into human-machine interfaces (HMIs) is in increasing demand for intuitive and effective manipulation. Traditional glove-based HMIs, constrained by nonconformal rigid structures or the need for bulky batteries, face limitations in continuous operation. Addressing this, we introduce yarn-based bend sensors in our smart glove, which are wirelessly powered and harvest energy from a fully textile 5.8 GHz WiFi-band antenna receiver. These sensors exhibit a gauge factor (GF) of 5.60 for strains ranging from 0 to 10%. They show a consistent performance regardless of the straining frequency when being stretched and released at frequencies between 0.1 and 0.7 Hz. This reliability ensures that the sensor output is solely dependent on the yarn's elongation. Accurately detecting finger-bending movements from 0° to 90° in a virtual environment, the sensors enable enhanced degrees of freedom for human finger interaction. When integrated with advanced machine-learning techniques, the system achieves a classification accuracy of 98.75% for object recognition, demonstrating its potential for precise and accurate HMI. Unlike conventional near-field energy transfer methods that rely on magnetic flux and are limited by power loss over distance, our fully textile design effectively harvests microwave energy, showing no voltage deterioration up to 1 m away. This minimalist microwave-powered smart glove represents a significant advancement, offering a viable and practical solution for developing intuitive and reliable HMIs.
将灵活传感器集成到人机界面 (HMI) 中对于直观有效的操作的需求日益增长。传统的基于手套的 HMIs 受到非顺应性刚性结构或对大型电池的需求的限制,在连续操作方面存在局限性。针对这一问题,我们在智能手套中引入了基于纱线的弯曲传感器,这些传感器由无线供电,并从全纺织 5.8GHz WiFi 波段天线接收器中获取能量。这些传感器在应变范围为 0 至 10%时表现出 5.60 的应变系数 (GF)。无论拉伸和释放频率如何,它们在 0.1 至 0.7Hz 的频率范围内拉伸时表现出一致的性能。这种可靠性确保传感器输出仅取决于纱线的伸长率。传感器能够准确检测虚拟环境中从 0°到 90°的手指弯曲运动,为人类手指交互提供更高的自由度。当与先进的机器学习技术集成时,该系统实现了 98.75%的物体识别分类准确率,展示了其在精确和准确的 HMI 方面的潜力。与传统的近场能量传输方法不同,后者依赖于磁通量并且受到距离上的功率损耗限制,我们的全纺织设计能够有效地收集微波能量,在 1 米远的距离内不会出现电压恶化。这种极简主义的微波供电智能手套是一项重大进展,为开发直观可靠的 HMIs 提供了可行且实用的解决方案。