Machine Learning Glove Using Self‐Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications
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 stra...
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| Published in | Advanced science Vol. 7; no. 14; pp. 2000261 - n/a |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Weinheim
John Wiley & Sons, Inc
01.07.2020
John Wiley and Sons Inc Wiley |
| Subjects | |
| Online Access | Get full text |
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| 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.
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. |
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| AbstractList | 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. 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. 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. 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.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. |
| Author | Zhang, Zixuan Wen, Feng Shi, Qiongfeng Zhang, Ting Zhu, Minglu Sun, Zhongda Lee, Chengkuo He, Tianyiyi Li, Lianhui |
| AuthorAffiliation | 2 National University of Singapore Suzhou Research Institute (NUSRI) Suzhou Industrial Park Suzhou 215123 China 5 i‐Lab Suzhou Institute of Nano‐Tech and Nano‐Bionics Chinese Academy of Sciences (CAS) Suzhou 215123 China 1 Department of Electrical & Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Singapore 3 Center for Intelligent Sensors and MEMS National University of Singapore 5 Engineering Drive 1 Singapore 117608 Singapore 4 Hybrid Integrated Flexible Electronic Systems (HIFES) 5 Engineering Drive 1 Singapore 117608 Singapore |
| AuthorAffiliation_xml | – name: 5 i‐Lab Suzhou Institute of Nano‐Tech and Nano‐Bionics Chinese Academy of Sciences (CAS) Suzhou 215123 China – name: 1 Department of Electrical & Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Singapore – name: 3 Center for Intelligent Sensors and MEMS National University of Singapore 5 Engineering Drive 1 Singapore 117608 Singapore – name: 4 Hybrid Integrated Flexible Electronic Systems (HIFES) 5 Engineering Drive 1 Singapore 117608 Singapore – name: 2 National University of Singapore Suzhou Research Institute (NUSRI) Suzhou Industrial Park Suzhou 215123 China |
| Author_xml | – sequence: 1 givenname: Feng surname: Wen fullname: Wen, Feng organization: Hybrid Integrated Flexible Electronic Systems (HIFES) – sequence: 2 givenname: Zhongda surname: Sun fullname: Sun, Zhongda organization: Hybrid Integrated Flexible Electronic Systems (HIFES) – sequence: 3 givenname: Tianyiyi surname: He fullname: He, Tianyiyi organization: Hybrid Integrated Flexible Electronic Systems (HIFES) – sequence: 4 givenname: Qiongfeng surname: Shi fullname: Shi, Qiongfeng organization: Hybrid Integrated Flexible Electronic Systems (HIFES) – sequence: 5 givenname: Minglu surname: Zhu fullname: Zhu, Minglu organization: Hybrid Integrated Flexible Electronic Systems (HIFES) – sequence: 6 givenname: Zixuan surname: Zhang fullname: Zhang, Zixuan organization: Hybrid Integrated Flexible Electronic Systems (HIFES) – sequence: 7 givenname: Lianhui surname: Li fullname: Li, Lianhui organization: Chinese Academy of Sciences (CAS) – sequence: 8 givenname: Ting surname: Zhang fullname: Zhang, Ting email: tzhang2009@sinano.ac.cn organization: Chinese Academy of Sciences (CAS) – sequence: 9 givenname: Chengkuo orcidid: 0000-0002-8886-3649 surname: Lee fullname: Lee, Chengkuo email: elelc@nus.edu.sg organization: Hybrid Integrated Flexible Electronic Systems (HIFES) |
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| Snippet | The rapid progress of Internet of things (IoT) technology raises an imperative demand on human machine interfaces (HMIs) which provide a critical linkage... Abstract The rapid progress of Internet of things (IoT) technology raises an imperative demand on human machine interfaces (HMIs) which provide a critical... |
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| SubjectTerms | Artificial intelligence gesture recognition Hydrophobic surfaces Internet of Things Machine learning Power supply Robotics Screen printing Sensors superhydrophobic textiles triboelectric nanogenerators (TENGs) virtual reality/augmented reality (VR/AR) controls |
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| Title | Machine Learning Glove Using Self‐Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications |
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