Please use this identifier to cite or link to this item: https://doi.org/10.1002/advs.202000261
Title: Machine Learning Glove Using Self-Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications
Authors: Wen, Feng
Sun, Zhongda
He, Tianyiyi 
Shi, Qiongfeng 
Zhu, Minglu
Zhang, Zixuan 
Li, Lianhui
Zhang, Ting
Lee, Chengkuo 
Keywords: gesture recognition
machine learning
superhydrophobic textiles
triboelectric nanogenerators (TENGs)
virtual reality/augmented reality (VR/AR) controls
Issue Date: 9-Jun-2020
Publisher: WILEY
Citation: Wen, Feng, Sun, Zhongda, He, Tianyiyi, Shi, Qiongfeng, Zhu, Minglu, Zhang, Zixuan, Li, Lianhui, Zhang, Ting, Lee, Chengkuo (2020-06-09). Machine Learning Glove Using Self-Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications. ADVANCED SCIENCE 7 (14). ScholarBank@NUS Repository. https://doi.org/10.1002/advs.202000261
Abstract: The rapid progress of Internet of things (IoT) technology raises an imperative demand on human machine interfaces (HMIs) which provide a critical linkage between human and machines. Using a glove as an intuitive and low-cost HMI can expediently track the motions of human fingers, resulting in a straightforward communication media of human–machine interactions. When combining several triboelectric textile sensors and proper machine learning technique, it has great potential to realize complex gesture recognition with the minimalist-designed glove for the comprehensive control in both real and virtual space. However, humidity or sweat may negatively affect the triboelectric output as well as the textile itself. Hence, in this work, a facile carbon nanotubes/thermoplastic elastomer (CNTs/TPE) coating approach is investigated in detail to achieve superhydrophobicity of the triboelectric textile for performance improvement. With great energy harvesting and human motion sensing capabilities, the glove using the superhydrophobic textile realizes a low-cost and self-powered interface for gesture recognition. By leveraging machine learning technology, various gesture recognition tasks are done in real time by using gestures to achieve highly accurate virtual reality/augmented reality (VR/AR) controls including gun shooting, baseball pitching, and flower arrangement, with minimized effect from sweat during operation.
Source Title: ADVANCED SCIENCE
URI: https://scholarbank.nus.edu.sg/handle/10635/189861
ISSN: 21983844
DOI: 10.1002/advs.202000261
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