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

作　　者：Yang Jiang Jie An Fei Liang Guoyu Zuo Jia Yi Chuan Ning Hong Zhang Kai Dong Zhong Lin Wang 

机构地区：[1]Beijing Institute of Nanoenergy and Nanosystems,Chinese Academy of Sciences,Beijing 101400,China [2]School of Nanoscience and Technology,University of Chinese Academy of Sciences,Beijing 100049,China [3]Institute of Textiles and Clothing,The Hong Kong Polytechnic University Hung Hom,Kowloon,Hong Kong 999077,China [4]Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China [5]School of Materials Science and Engineering,Georgia Institute of Technology,Atlanta,GA 30332-0245,USA

出　　处：《Nano Research》2022年第9期8389-8397,共9页纳米研究（英文版）

基　　金：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);the Fundamental Research Funds for the Central Universities(No.E1E46805).

摘　　要：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.

关 键 词：posture monitoring knitted fabric triboelectric nanogenerator wearable electronics machine learning 

分 类 号：TS106[轻工技术与工程—纺织工程] TP181[轻工技术与工程—纺织科学与工程]