Machine Learning Glove Using Self‐Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications

Autor: Zixuan Zhang, Chengkuo Lee, Ting Zhang, Tianyiyi He, Feng Wen, Minglu Zhu, Qiongfeng Shi, Lianhui Li, Zhongda Sun
Rok vydání: 2020
Předmět:
Computer science
General Chemical Engineering
Interface (computing)
General Physics and Astronomy
Medicine (miscellaneous)
02 engineering and technology
Virtual reality
010402 general chemistry
Machine learning
computer.software_genre
01 natural sciences
Biochemistry
Genetics and Molecular Biology (miscellaneous)
General Materials Science
Human–machine system
lcsh:Science
Triboelectric effect
Full Paper
gesture recognition
business.industry
General Engineering
Full Papers
021001 nanoscience & nanotechnology
triboelectric nanogenerators (TENGs)
0104 chemical sciences
machine learning
virtual reality/augmented reality (VR/AR) controls
superhydrophobic textiles
Gesture recognition
lcsh:Q
Augmented reality
Artificial intelligence
Performance improvement
0210 nano-technology
business
computer
Gesture
Zdroj: Advanced Science
Advanced Science, Vol 7, Iss 14, Pp n/a-n/a (2020)
ISSN: 2198-3844
Popis: 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.
With capabilities of humidity‐resistant and anti‐sweat, high‐accuracy complicated gesture recognition based on machine learning is realized using a low‐cost and self‐powered superhydrophobic glove human machine interface (HMI) with minimized sweat effect. With gesture recognition, the virtual reality/augmented reality (VR/AR) controls including shooting, baseball pitching, and floral arrangement are successfully demonstrated.
Databáze: OpenAIRE
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