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Research Article

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

Yang Jiang1,2,§, Jie An1,2,§, Fei Liang3,§, Guoyu Zuo4, Jia Yi1,2, Chuan Ning1,2, Hong Zhang4, Kai Dong1,2( ), Zhong Lin Wang1,2,5( )
Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
Institute of Textiles and Clothing, The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong 999077, China
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0245, USA

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

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Graphical Abstract

This study proposes a portable and convenient self-powered sitting position monitoring vest (SPMV) that reminds users to maintain the right posture during a sustained working period. The SPMV exhibits a high sensitivity, excellent mechanical stretchability, good air permeability, and a posture recognition accuracy of 96.6% using the random forest classifier.

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.

Keywords

posture monitoringknitted fabrictriboelectric nanogeneratorwearable electronicsmachine learning

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Nano Research
Volume 15 Issue 9,
September 2022
Pages 8389-8397
DOI: 10.1007/s12274-022-4409-0
Cite this article:
Jiang Y, An J, Liang F, et al. Knitted self-powered sensing textiles for machine learning-assisted sitting posture monitoring and correction. Nano Research, 2022, 15(9): 8389-8397. https://doi.org/10.1007/s12274-022-4409-0
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Received: 16 February 2022
Revised: 06 April 2022
Accepted: 07 April 2022
Published: 16 February 2022
© Tsinghua University Press 2022
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