Deep Learning-Assisted Sensitive 3C-SiC Sensor for Long-Term Monitoring of Physical Respiration
Article
| Article Title | Deep Learning-Assisted Sensitive 3C-SiC Sensor for Long-Term Monitoring of Physical Respiration |
|---|---|
| Article Category | Article |
| Authors | Tran, Thi Lap, Nguyen, Duy Van, Nguyen, Hung, Nguyen, Thi Phuoc Van, Song, Pingan, Deo, Ravinesh C, Moloney, Clint, Dao, Viet Dung, Nguyen, Nam-Trung and Dinh, Toan |
| Journal Title | Advanced Sensor Research |
| Journal Citation | 3 (8) |
| Article Number | 2300159 |
| Number of Pages | 10 |
| Year | 2024 |
| Publisher | John Wiley & Sons |
| Place of Publication | Germany |
| ISSN | 2751-1219 |
| Digital Object Identifier (DOI) | https://doi.org/10.1002/adsr.202300159 |
| Web Address (URL) | https://onlinelibrary.wiley.com/doi/epdf/10.1002/adsr.202300159 |
| Abstract | In human life, respiration serves as a crucial physiological signal. Continuous real-time respiration monitoring can provide valuable insights for the early detection and management of several respiratory diseases. High-sensitivity, noninvasive, comfortable, and long-term stable respiration devices are highly desirable. In spite of this, existing respiration sensors cannot provide continuous long-term monitoring due to the erosion from moisture, fluctuations in body temperature, and many other environmental factors. This research developed a wearable thermal-based respiration sensor made of cubic silicon carbide (3C-SiC) using a microfabrication process. The results showed that as a result of the Joule heating effect in the robustness 3C-SiCmaterial, the sensor offered high sensitivity with the negative temperature coefficient of resistance of approximately 5,200ppmK -1 , an excellent response to respiration and long-term stability monitoring. Furthermore, by incorporating a deep learning model, this fabricated sensor can develop advanced capabilities to distinguish between the four distinct breath patterns: slow, normal, fast, and deep breathing, and provide an impressive classification accuracy rate of ≈ 99.7%. The results of this research represent a significant step in developing wearable respiration sensors for personal healthcare systems. |
| Keywords | Deep Learning-Assisted Sensitive 3C-SiC Sensor |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 461103. Deep learning |
| Byline Affiliations | Centre for Future Materials |
| Thanh Do University, Vietnam | |
| School of Mathematics, Physics and Computing | |
| School of Nursing and Midwifery | |
| Griffith University |
https://research.usq.edu.au/item/z7692/deep-learning-assisted-sensitive-3c-sic-sensor-for-long-term-monitoring-of-physical-respiration
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| Advanced Sensor Research - 2024 - Tran - Deep Learning‐Assisted Sensitive 3C‐SiC Sensor for Long‐Term Monitoring of.pdf | ||
| License: CC BY 4.0 | ||
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