Zhu Minglu, Sun Zhongda, Zhang Zixuan, Shi Qiongfeng, He Tianyiyi, Liu Huicong, Chen Tao, Lee Chengkuo
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore.
Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore 117608, Singapore.
Sci Adv. 2020 May 8;6(19):eaaz8693. doi: 10.1126/sciadv.aaz8693. eCollection 2020 May.
Human-machine interfaces (HMIs) experience increasing requirements for intuitive and effective manipulation. Current commercialized solutions of glove-based HMI are limited by either detectable motions or the huge cost on fabrication, energy, and computing power. We propose the haptic-feedback smart glove with triboelectric-based finger bending sensors, palm sliding sensor, and piezoelectric mechanical stimulators. The detection of multidirectional bending and sliding events is demonstrated in virtual space using the self-generated triboelectric signals for various degrees of freedom on human hand. We also perform haptic mechanical stimulation via piezoelectric chips to realize the augmented HMI. The smart glove achieves object recognition using machine learning technique, with an accuracy of 96%. Through the integrated demonstration of multidimensional manipulation, haptic feedback, and AI-based object recognition, our glove reveals its potential as a promising solution for low-cost and advanced human-machine interaction, which can benefit diversified areas, including entertainment, home healthcare, sports training, and medical industry.
人机接口(HMIs)对直观且有效的操作的要求日益提高。当前基于手套的商业化人机接口解决方案,要么受限于可检测的动作,要么在制造、能源和计算能力方面成本高昂。我们提出了一种具有基于摩擦电的手指弯曲传感器、手掌滑动传感器和压电机械刺激器的触觉反馈智能手套。利用人体手部不同自由度的自发电摩擦电信号,在虚拟空间中展示了对多方向弯曲和滑动事件的检测。我们还通过压电芯片进行触觉机械刺激,以实现增强型人机接口。该智能手套利用机器学习技术实现物体识别,准确率达96%。通过多维操作、触觉反馈和基于人工智能的物体识别的综合展示,我们的手套展现出其作为低成本且先进的人机交互解决方案的潜力,这可惠及包括娱乐、家庭医疗保健、体育训练和医疗行业在内的多个领域。