A Highly Sensitive Coaxial Nanofiber Mask for Respiratory Monitoring Assisted with Machine Learning  

作　　者：Boling Lan Cheng Zhong Shenglong Wang Yong Ao Yang Liu Yue Sun Tao Yang Guo Tian Longchao Huang Jieling Zhang Weili Deng Weiqing Yang 

机构地区：[1]Key Laboratory of Advanced Technologies of Materials(Ministry of Education),School of Materials Science and Engineering,Southwest Jiaotong University,Chengdu,Sichuan 610031,People’s Republic of China [2]Research Institute of Frontier Science,Southwest Jiaotong University,Chengdu,Sichuan 610031,People’s Republic of China

出　　处：《Advanced Fiber Materials》2024年第5期1402-1412,共11页先进纤维材料（英文）

基　　金：supported by the Sichuan Science and Technology Program(No.2023NSFSC0313);the Basic Research Cultivation Project of Southwest Jiaotong University(No.2682023KJ024).

摘　　要：Respiration is a critical physiological process of the body and plays an essential role in maintaining human health.Wearable piezoelectric nanofiber-based respiratory monitoring has attracted much attention due to its self-power,high linearity,noninvasiveness,and convenience.However,the limited sensitivity of conventional piezoelectric nanofibers makes it difficult to meet medical and daily respiratory monitoring requirements due to their low electromechanical conversion efficiency.Here,we present a universally applicable,highly sensitive piezoelectric nanofiber characterized by a coaxial composite structure of polyvinylidene fluoride(PVDF)and carbon nanotube(CNT),which is denoted as PS-CC.Based on elucidating the enhancement mechanism from the percolation effect,PS-CC exhibits excellent sensing performance with a high sensitivity of 3.7 V/N and a fast response time of 20 ms for electromechanical conversion.As a proof-of-concept,the nanofiber membrane is seamlessly integrated into a facial mask,facilitating accurate recognition of respiratory states.With the assistance of a one-dimensional convolutional neural network(CNN),a PS-CC-based smart mask can recognize respiratory tracts and multiple breathing patterns with a classification accuracy of up to 97.8%.Notably,this work provides an effective strategy for monitoring respiratory diseases and offers widespread utility for daily health monitoring and clinical applications.

关 键 词：PVDF/CNT Coaxial nanofiber HIGH-SENSITIVITY Smart mask Machine learning Respiratory monitoring 

分 类 号：TB383.1[一般工业技术—材料科学与工程] TP181[自动化与计算机技术—控制理论与控制工程]