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

作者机构：Beijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 101400China School of Nanoscience and TechnologyUniversity of Chinese Academy of SciencesBeijing 100049China Institute of Textiles and ClothingThe Hong Kong Polytechnic University Hung HomKowloonHong Kong 999077China Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China School of Materials Science and EngineeringGeorgia Institute of TechnologyAtlantaGA 30332-0245USA 

出 版 物：《Nano Research》 (纳米研究（英文版）)

年 卷 期：2022年第15卷第9期

页      面：8389-8397页

核心收录：

学科分类：0821[工学-纺织科学与工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 082102[工学-纺织材料与纺织品设计] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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) 

主　　题：posture monitoring knitted fabric triboelectric nanogenerator wearable electronics machine learning 

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 *** 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 *** 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 *** 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 *** 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%)*** 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.