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Knitted self-powered sensing textiles for machine learning-assisted sitting posture monitoring and correction

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Abstract

With increasing work pressure in modern society, prolonged sedentary positions with poor sitting postures can cause physical and psychological problems, including obesity, muscular disorders, and myopia. In this paper, we present a self-powered sitting position monitoring vest (SPMV) based on triboelectric nanogenerators (TENGs) to achieve accurate real-time posture recognition through an integrated machine learning algorithm. The SPMV achieves high sensitivity (0.16 mV/Pa), favorable stretchability (10%), good stability (12,000 cycles), and machine washability (10 h) by employing knitted double threads interlaced with conductive fiber and nylon yarn. Utilizing a knitted structure and sensor arrays that are stitched into different parts of the clothing, the SPMV offers a non-invasive method of recognizing different sitting postures, providing feedback, and warning users while enhancing long-term wearing comfortability. It achieves a posture recognition accuracy of 96.6% using the random forest classifier, which is higher than the logistic regression (95.5%) and decision tree (94.3%) classifiers. The TENG-based SPMV offers a reliable solution in the healthcare system for non-invasive and long-term monitoring, promoting the development of triboelectric-based wearable electronics.

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Acknowledgements

The authors are grateful for the support received from the National Key R&D Program of China (No. 2021YFA1201601), the National Natural Science Foundation of China (No. 22109012), Natural Science Foundation of Beijing (No. 2212052), and the Fundamental Research Funds for the Central Universities (No. E1E46805).

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Author notes

  1. Yang Jiang, Jie An, and Fei Liang contributed equally to this work.

Authors and Affiliations

  1. Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China

    Yang Jiang, Jie An, Jia Yi, Chuan Ning, Kai Dong & Zhong Lin Wang

  2. School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China

    Yang Jiang, Jie An, Jia Yi, Chuan Ning, Kai Dong & Zhong Lin Wang

  3. Institute of Textiles and Clothing, The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong, 999077, China

    Fei Liang

  4. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Guoyu Zuo & Hong Zhang

  5. School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0245, USA

    Zhong Lin Wang

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  1. Yang Jiang
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  2. Jie An
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Correspondence to Kai Dong or Zhong Lin Wang.

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Jiang, Y., An, J., Liang, F. et al. Knitted self-powered sensing textiles for machine learning-assisted sitting posture monitoring and correction. Nano Res. 15, 8389–8397 (2022). https://doi.org/10.1007/s12274-022-4409-0

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