A Deep-Learning-Assisted On-Mask Sensor Network for Adaptive Respiratory Monitoring
© 2022 Wiley-VCH GmbH.
| Veröffentlicht in: | Advanced materials (Deerfield Beach, Fla.). - 1998. - 34(2022), 24 vom: 21. Juni, Seite e2200252 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
|
| Zugriff auf das übergeordnete Werk: | Advanced materials (Deerfield Beach, Fla.) |
| Schlagworte: | Journal Article Rayleigh instabilities deep learning on-mask sensor networks personalized healthcare respiratory monitoring |
| Zusammenfassung: | © 2022 Wiley-VCH GmbH. 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-1 . 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 |
|---|---|
| Beschreibung: | Date Completed 17.06.2022 Date Revised 17.06.2022 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1521-4095 |
| DOI: | 10.1002/adma.202200252 |