Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Adv Mater. 2022 Jun;34(24):e2200252. doi: 10.1002/adma.202200252. Epub 2022 May 16.
Wearable respiratory monitoring is a fast, non-invasive, and convenient approach to provide early recognition of human health abnormalities like restrictive and obstructive lung diseases. Here, a computational fluid dynamics assisted on-mask sensor network is reported, which can overcome different user facial contours and environmental interferences to collect highly accurate respiratory signals. Inspired by cribellate silk, Rayleigh-instability-induced spindle-knot fibers are knitted for the fabrication of permeable and moisture-proof textile triboelectric sensors that hold a decent signal-to-noise ratio of 51.2 dB, a response time of 0.28 s, and a sensitivity of 0.46 V kPa . With the assistance of deep learning, the on-mask sensor network can realize the respiration pattern recognition with a classification accuracy up to 100%, showing great improvement over a single respiratory sensor. Additionally, a customized user-friendly cellphone application is developed to connect the processed respiratory signals for real-time data-driven diagnosis and one-click health data sharing with the clinicians. The deep-learning-assisted on-mask sensor network opens a new avenue for personalized respiration management in the era of the Internet of Things.
可穿戴式呼吸监测是一种快速、非侵入性且便捷的方法，可用于早期识别人体健康异常，如限制性和阻塞性肺部疾病。在这里，我们报告了一种基于计算流体动力学辅助的口罩传感器网络，它可以克服不同用户面部轮廓和环境干扰，以收集高度准确的呼吸信号。受蛛丝启发，利用瑞利不稳定性诱导的纺锤结纤维编织而成的透气、防潮纺织摩擦电传感器，具有良好的信噪比为 51.2dB、响应时间为 0.28s 和灵敏度为 0.46VkPa。借助深度学习，口罩传感器网络可以实现呼吸模式识别，分类准确率高达 100%，比单个呼吸传感器有了很大的提高。此外，还开发了一个定制的用户友好型手机应用程序，用于连接处理后的呼吸信号，以便进行实时数据驱动诊断，并与临床医生一键共享健康数据。基于深度学习的口罩传感器网络为物联网时代的个性化呼吸管理开辟了新途径。
