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

IF 17.2 1区工程技术 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Advanced Fiber Materials Pub Date : 2024-05-14 DOI:10.1007/s42765-024-00420-w

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

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Abstract

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.

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利用机器学习辅助呼吸监测的高灵敏度同轴纳米纤维面罩

呼吸是人体的一个重要生理过程，对维持人体健康起着至关重要的作用。基于可穿戴压电纳米纤维的呼吸监测因其自供电、高线性度、无创性和便捷性而备受关注。然而，传统压电纳米纤维的灵敏度有限，机电转换效率低，难以满足医疗和日常呼吸监测的要求。在此，我们提出了一种普遍适用的高灵敏度压电纳米纤维，其特征在于聚偏二氟乙烯（PVDF）和碳纳米管（CNT）的同轴复合结构，简称 PS-CC。在阐明渗流效应增强机制的基础上，PS-CC 表现出优异的传感性能，灵敏度高达 3.7 V/N，机电转换响应时间快达 20 ms。作为概念验证，纳米纤维膜与面罩无缝集成，有助于准确识别呼吸状态。在一维卷积神经网络（CNN）的辅助下，基于 PS-CC 的智能面罩可以识别呼吸道和多种呼吸模式，分类准确率高达 97.8%。值得注意的是，这项工作为监测呼吸系统疾病提供了一种有效的策略，并为日常健康监测和临床应用提供了广泛的实用性。

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来源期刊

Advanced Fiber Materials Multiple-

CiteScore

18.70

自引率

11.20%

发文量

109

期刊介绍： Advanced Fiber Materials is a hybrid, peer-reviewed, international and interdisciplinary research journal which aims to publish the most important papers in fibers and fiber-related devices as well as their applications.Indexed by SCIE, EI, Scopus et al. Publishing on fiber or fiber-related materials, technology, engineering and application.