Triboelectric‐Inertial Sensing Glove Enhanced by Charge‐Retained Strategy for Human‐Machine Interaction
Abstract As technology advances, human‐machine interaction (HMI) demands more intuitive and natural methods. To meet this demand, smart gloves, capable of capturing intricate hand movements, are emerging as vital HMI tools. Moreover, triboelectric‐based sensors, with their self‐powered, cost‐effecti...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202408689 |
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| author | Bo Yang Jia Cheng Xuecheng Qu Yuning Song Lifa Yang Junyao Shen Ziqian Bai Linhong Ji |
| author_facet | Bo Yang Jia Cheng Xuecheng Qu Yuning Song Lifa Yang Junyao Shen Ziqian Bai Linhong Ji |
| author_sort | Bo Yang |
| collection | DOAJ |
| description | Abstract As technology advances, human‐machine interaction (HMI) demands more intuitive and natural methods. To meet this demand, smart gloves, capable of capturing intricate hand movements, are emerging as vital HMI tools. Moreover, triboelectric‐based sensors, with their self‐powered, cost‐effective, and material various characteristics, can offer promising solutions for enhancing existing glove systems. However, a key limitation of these sensors is that charge leakage in the measurement circuit results in capturing only transient signals, rather than continuous changes. To address this issue, a charge‐retained circuit that effectively prevents triboelectric signal attenuation is developed, enabling accurate measurement of continuous finger movements. This innovation forms the foundation of a highly integrated smart glove system, enhancing HMI functionality by combining continuous triboelectric signals with inertial sensor data. The system showcases a diverse range of applications, including complex robotic control, virtual reality interaction, smart home lighting adjustments, and intuitive interface operations. Furthermore, by leveraging artificial intelligence (AI) techniques, the system achieves accurate recognition of complex sign language with an impressive 99.38% accuracy. This work presents a charge‐retained approach for continuous sensing with triboelectric‐based sensors, offering valuable insights for developing future multifunctional HMI and sign language recognition systems. The proposed smart glove system, with its dual‐mode sensing and AI integration, holds great potential for revolutionizing various domains and enhancing user experiences. |
| format | Article |
| id | doaj-art-f67e2e3ac09a42f7a3820b54ca161553 |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-f67e2e3ac09a42f7a3820b54ca1615532025-01-20T13:04:18ZengWileyAdvanced Science2198-38442025-01-01123n/an/a10.1002/advs.202408689Triboelectric‐Inertial Sensing Glove Enhanced by Charge‐Retained Strategy for Human‐Machine InteractionBo Yang0Jia Cheng1Xuecheng Qu2Yuning Song3Lifa Yang4Junyao Shen5Ziqian Bai6Linhong Ji7State Key Laboratory of Tribology in Advanced Equipment Department of Mechanical Engineering Tsinghua University Beijing 100084 ChinaState Key Laboratory of Tribology in Advanced Equipment Department of Mechanical Engineering Tsinghua University Beijing 100084 ChinaState Key Laboratory of Tribology in Advanced Equipment Department of Mechanical Engineering Tsinghua University Beijing 100084 ChinaBeijing Lvkedu Science and Technology Co. Ltd. Beijing 100190 ChinaState Key Laboratory of Tribology in Advanced Equipment Department of Mechanical Engineering Tsinghua University Beijing 100084 ChinaState Key Laboratory of Tribology in Advanced Equipment Department of Mechanical Engineering Tsinghua University Beijing 100084 ChinaState Key Laboratory of Tribology in Advanced Equipment Department of Mechanical Engineering Tsinghua University Beijing 100084 ChinaState Key Laboratory of Tribology in Advanced Equipment Department of Mechanical Engineering Tsinghua University Beijing 100084 ChinaAbstract As technology advances, human‐machine interaction (HMI) demands more intuitive and natural methods. To meet this demand, smart gloves, capable of capturing intricate hand movements, are emerging as vital HMI tools. Moreover, triboelectric‐based sensors, with their self‐powered, cost‐effective, and material various characteristics, can offer promising solutions for enhancing existing glove systems. However, a key limitation of these sensors is that charge leakage in the measurement circuit results in capturing only transient signals, rather than continuous changes. To address this issue, a charge‐retained circuit that effectively prevents triboelectric signal attenuation is developed, enabling accurate measurement of continuous finger movements. This innovation forms the foundation of a highly integrated smart glove system, enhancing HMI functionality by combining continuous triboelectric signals with inertial sensor data. The system showcases a diverse range of applications, including complex robotic control, virtual reality interaction, smart home lighting adjustments, and intuitive interface operations. Furthermore, by leveraging artificial intelligence (AI) techniques, the system achieves accurate recognition of complex sign language with an impressive 99.38% accuracy. This work presents a charge‐retained approach for continuous sensing with triboelectric‐based sensors, offering valuable insights for developing future multifunctional HMI and sign language recognition systems. The proposed smart glove system, with its dual‐mode sensing and AI integration, holds great potential for revolutionizing various domains and enhancing user experiences.https://doi.org/10.1002/advs.202408689artificial intelligencegesture recognitionhuman‐machine interaction (HMI)signal processingsmart glovetriboelectric sensor |
| spellingShingle | Bo Yang Jia Cheng Xuecheng Qu Yuning Song Lifa Yang Junyao Shen Ziqian Bai Linhong Ji Triboelectric‐Inertial Sensing Glove Enhanced by Charge‐Retained Strategy for Human‐Machine Interaction Advanced Science artificial intelligence gesture recognition human‐machine interaction (HMI) signal processing smart glove triboelectric sensor |
| title | Triboelectric‐Inertial Sensing Glove Enhanced by Charge‐Retained Strategy for Human‐Machine Interaction |
| title_full | Triboelectric‐Inertial Sensing Glove Enhanced by Charge‐Retained Strategy for Human‐Machine Interaction |
| title_fullStr | Triboelectric‐Inertial Sensing Glove Enhanced by Charge‐Retained Strategy for Human‐Machine Interaction |
| title_full_unstemmed | Triboelectric‐Inertial Sensing Glove Enhanced by Charge‐Retained Strategy for Human‐Machine Interaction |
| title_short | Triboelectric‐Inertial Sensing Glove Enhanced by Charge‐Retained Strategy for Human‐Machine Interaction |
| title_sort | triboelectric inertial sensing glove enhanced by charge retained strategy for human machine interaction |
| topic | artificial intelligence gesture recognition human‐machine interaction (HMI) signal processing smart glove triboelectric sensor |
| url | https://doi.org/10.1002/advs.202408689 |
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