A Mask-Shaped Respiration Sensor Using Triboelectricity and a Machine Learning Approach toward Smart Sleep Monitoring Systems
Abstract
:1. Introduction
2. Materials and Methods
2.1. Fabrication of the Mask-Shaped Triboelectric Nanogenerator (M-TENG)
2.2. Working Mechanism of the M-TENG
2.3. Characterization
2.4. Measurement Setups
2.5. Human Subject Study
3. Experimental Results
3.1. Characteristics and Electrical Output of the Fabricated M-TENG
3.2. Electrical Signal Collection According to the Sleep Stage
3.3. Data Process of the Proposed System with the K-Mean Clustering and Classification Results
4. Discussion
4.1. Effect of Humidity in the Electrical Output Generated from the M-TENG
4.2. Effect of the Tilted RIE Process into the Electrical Output Generated from the M-TENG
4.3. Optimizing the Parameter for Increasing Classification Accuracy of the Proposed System
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Yun, J.; Park, J.; Jeong, S.; Hong, D.; Kim, D. A Mask-Shaped Respiration Sensor Using Triboelectricity and a Machine Learning Approach toward Smart Sleep Monitoring Systems. Polymers 2022, 14, 3549. https://doi.org/10.3390/polym14173549
Yun J, Park J, Jeong S, Hong D, Kim D. A Mask-Shaped Respiration Sensor Using Triboelectricity and a Machine Learning Approach toward Smart Sleep Monitoring Systems. Polymers. 2022; 14(17):3549. https://doi.org/10.3390/polym14173549
Chicago/Turabian StyleYun, Jonghyeon, Jihyeon Park, Suna Jeong, Deokgi Hong, and Daewon Kim. 2022. "A Mask-Shaped Respiration Sensor Using Triboelectricity and a Machine Learning Approach toward Smart Sleep Monitoring Systems" Polymers 14, no. 17: 3549. https://doi.org/10.3390/polym14173549