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

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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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Acknowledgements

This research was financially supported by the Sichuan Science and Technology Program (No. 2023NSFSC0313) and the Basic Research Cultivation Project of Southwest Jiaotong University (No. 2682023KJ024). We are thankful for the help from the Analysis and Testing Center of Southwest Jiaotong University.

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Authors and Affiliations

  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

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

  2. Research Institute of Frontier Science, Southwest Jiaotong University, Chengdu, Sichuan, 610031, People’s Republic of China

    Weiqing Yang

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  1. Boling Lan
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Lan, B., Zhong, C., Wang, S. et al. A Highly Sensitive Coaxial Nanofiber Mask for Respiratory Monitoring Assisted with Machine Learning. Adv. Fiber Mater. (2024). https://doi.org/10.1007/s42765-024-00420-w

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