On-Mask Magnetoelastic Sensor Network for Self-Powered Respiratory Monitoring
Runlin Wang, Yifei Du, Xiao Wan, Jing Xu, Jun Chen
Abstract
Respiratory monitoring is crucial because it provides key insights into a person's health and physiological conditions. Conventional respiratory sensing is significantly challenged by the presence of water vapor in exhaled breath. An on-mask magnetoelastic sensor network is developed, featuring an ultralight, intrinsically waterproof architecture to achieve continuous, long-term respiratory monitoring and real-time, high-fidelity signal acquisition. Leveraging the giant magnetoelastic effect, each soft magnetoelastic sensor is miniaturized to only 3.2 g, which markedly enhances its sensitivity to airflow-induced mechanical fluctuations during respiration while also ensuring sufficient wearing comfort for daily use. Beyond mechanical compliance, the system achieves a signal-to-noise ratio exceeding 35 dB and a rapid response time of 80 ms under the optimal conditions, and it can reliably transduce the fluid dynamics generated during respiration in the mouth-mask microenvironment into high-fidelity electrical signals for continuous respiratory monitoring. With the aid of machine learning, the on-mask magnetoelastic sensor network achieves respiration pattern recognition with a classification accuracy of up to 94.03%. Furthermore, a user-friendly, custom-designed mobile application has been developed to process respiratory signals, enabling real-time, data-driven diagnosis and one-click health data sharing with clinicians. This machine-learning-enhanced magnetoelastic sensor network is expected to support personalized respiratory management in the Internet of Things era.