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Nanogenerator-Based Wireless Intelligent Motion Correction System for Storing Mechanical Energy of Human Motion

Author

Listed:
  • Yupeng Mao

    (Physical Education Department, Northeastern University, Shenyang 110819, China)

  • Fengxin Sun

    (Physical Education Department, Northeastern University, Shenyang 110819, China)

  • Yongsheng Zhu

    (Physical Education Department, Northeastern University, Shenyang 110819, China)

  • Changjun Jia

    (Physical Education Department, Northeastern University, Shenyang 110819, China)

  • Tianming Zhao

    (College of Sciences, Northeastern University, Shenyang 110819, China)

  • Chaorui Huang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Caixia Li

    (Physical Education Department, Northeastern University, Shenyang 110819, China)

  • Ning Ba

    (Department of Sports Science and Physical Education, Tsinghua University, Beijing 100084, China)

  • Tongtong Che

    (Capital University of Physical Education and Sport, Beijing 100191, China
    College of Physical Education, Qingdao University, Qingdao 266071, China)

  • Song Chen

    (Physical Education Department, Northeastern University, Shenyang 110819, China)

Abstract

As it is urgently needed to address the energy consumption and health care problems caused by population growth, the field of sustainable energy collection and storage equipment as well as intelligent health care for monitoring human motion behavior has received wide attention and achieved rapid development. However, the portable intelligent systems that integrate them have not been widely discussed. In this work, we propose a design of a nanogenerator-based wireless intelligent motion correction system, combining triboelectric nanogenerator technology with wireless intelligent host computer signal processing and visualization systems. Under the condition of no external power supply, a noninvasive triboelectric nanogenerator (FL-TENG) sensor integrated system stores the mechanical energy due to human movement behavior and drives wireless micro-electronic devices to realize the human–computer interaction application of the intelligent system. In the conducted test, the reported instantaneous output of an ordinary clap action was around 241V. For a variety of physical exercise types being monitored, it can accurately determine human movement behavior and perform error correction and scoring for movement techniques. Additionally, using hydrogel as an electrode improves the service life and stability of the device. Therefore, this flexible and convenient design concept is beneficial to the development and utilization of sustainable energy and sports activities. In addition, it extends the application prospects of FL-TENG in self-powered sensing systems.

Suggested Citation

  • Yupeng Mao & Fengxin Sun & Yongsheng Zhu & Changjun Jia & Tianming Zhao & Chaorui Huang & Caixia Li & Ning Ba & Tongtong Che & Song Chen, 2022. "Nanogenerator-Based Wireless Intelligent Motion Correction System for Storing Mechanical Energy of Human Motion," Sustainability, MDPI, vol. 14(11), pages 1-12, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6944-:d:832836
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    References listed on IDEAS

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    1. Ranran Liu & Xiaobin Dong & Peng Zhang & Ying Zhang & Xiaowan Wang & Ya Gao, 2020. "Study on the Sustainable Development of an Arid Basin Based on the Coupling Process of Ecosystem Health and Human Wellbeing Under Land Use Change—A Case Study in the Manas River Basin, Xinjiang, China," Sustainability, MDPI, vol. 12(3), pages 1-25, February.
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    1. Caixia Li & Yongsheng Zhu & Fengxin Sun & Changjun Jia & Tianming Zhao & Yupeng Mao & Haidong Yang, 2022. "Research Progress on Triboelectric Nanogenerator for Sports Applications," Energies, MDPI, vol. 15(16), pages 1-15, August.
    2. Changjun Jia & Yongsheng Zhu & Fengxin Sun & Yuzhang Wen & Qi Wang & Ying Li & Yupeng Mao & Chongle Zhao, 2022. "Gas-Supported Triboelectric Nanogenerator Based on In Situ Gap-Generation Method for Biomechanical Energy Harvesting and Wearable Motion Monitoring," Sustainability, MDPI, vol. 14(21), pages 1-13, November.
    3. Yongsheng Zhu & Fengxin Sun & Changjun Jia & Chaorui Huang & Kuo Wang & Ying Li & Liping Chou & Yupeng Mao, 2022. "A 3D Printing Triboelectric Sensor for Gait Analysis and Virtual Control Based on Human–Computer Interaction and the Internet of Things," Sustainability, MDPI, vol. 14(17), pages 1-12, August.

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