Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis

作者：Kaijun Zhang, Zhaoyang Li, Jianfeng Zhang, Dazhe Zhao, Yucong Pi, Yujun Shi, Renkun Wang, Peisheng Chen, Chaojie Li, Gangjin Chen, Iek Man Lei, Junwen Zhong · 发表于：ACS Sensors · 年份：2022 · DOI：10.1021/acssensors.2c01628 · 被引用次数：63 · 研究领域：Advanced Chemical Sensor Technologies、Advanced Sensor and Energy Harvesting Materials、Infection Control and Ventilation

Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respiratory diseases. In this work, a smart face mask is proposed with an air-permeable and biodegradable self-powered breath sensor as the key component. This smart face mask is easily fabricated, comfortable to use, eco-friendly, and has sensitive and stable output performances in real wearable conditions. To verify the practicability, we use smart face masks to record respiratory signals of patients with chronic respiratory diseases when the patients do not have obvious symptoms. With the assistance of the machine learning algorithm of the bagged decision tree, the accuracy for distinguishing the healthy group and three groups of chronic respiratory diseases (asthma, bronchitis, and chronic obstructive pulmonary disease) is up to 95.5%. These results indicate that the strategy of this work is feasible and may promote the development of wearable health monitoring systems.