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Human movement monitoring and behavior recognition for intelligent sports using customizable and flexible triboelectric nanogenerator

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Abstract

Effective collection, recognition, and analysis of sports information is the key to intelligent sports, which can help athletes to improve their skills and formulate scientific training plans and competition strategies. At present, wearable electronic devices used for movement monitoring still have some limitations, such as high cost and energy consumption, incompatibility of suitable flexibility and personalized spatial structure, dissatisfactory data analysis methods, etc. In this work, a novel three-dimensional-printed thermoplastic polyurethane is introduced as the elastic shell and friction layer, and it endows the proposed customizable and flexible triboelectric nanogenerator (CF-TENG) with personalized spatial structure and robust correlation to external pressure. In practical application, it exhibits highly sensitive responses to the joint-bending motion of the finger, wrist, or elbow. Furthermore, a pressure-sensing insole and smart ski pole based on CF-TENG are manufactured to build a comprehensive sports monitoring system to transmit the athletes’ motion information from feet and hands through the plantar pressure distribution and ski pole action. To recognize the movement status, the self-developed automatic peak recognition algorithm (P-Find) and machine learning algorithm (subspace K-Nearest Neighbors) were introduced to accurately distinguish the four typical motion behaviors and three primary sub-techniques of cross-country skiing, with accuracy rates of 98.2% and 100%. This work provides a novel strategy to promote the personalized applications of TENGs in intelligent sports.

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Authors and Affiliations

  1. Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan, 030051, China

    Yun Yang, XiaoJuan Hou, WenPing Geng, JiLiang Mu, Le Zhang, Jian He, JiJun Xiong & XiuJian Chou

  2. Department of Mining Engineering, Luliang University, Lvliang, 033000, China

    Yun Yang

  3. Physical Education Institute, Jimei University, Xiamen, 361021, China

    XiangDong Wang

Authors
  1. Yun Yang
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  2. XiaoJuan Hou
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  4. JiLiang Mu
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  5. Le Zhang
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  6. XiangDong Wang
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  7. Jian He
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  8. JiJun Xiong
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  9. XiuJian Chou
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Corresponding author

Correspondence to Jian He.

Additional information

This work was supported by the National Key R&D Program of China (Grant Nos. 2019YFF0301802, 2019YFB2004802, and 2018YFF0300605), National Natural Science Foundation of China (Grant Nos. 51975541 and 51975542), Applied Fundamental Research Program of Shanxi Province (Grant No. 201901D211281), National Defense Fundamental Research Project and Program for the Innovative Talents of Higher Education Institutions of Shanxi.

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Yang, Y., Hou, X., Geng, W. et al. Human movement monitoring and behavior recognition for intelligent sports using customizable and flexible triboelectric nanogenerator. Sci. China Technol. Sci. 65, 826–836 (2022). https://doi.org/10.1007/s11431-021-1984-9

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  • DOI: https://doi.org/10.1007/s11431-021-1984-9

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