Deep Learning‐Assisted Sensitive 3C‐SiC Sensor for Long‐Term Monitoring of Physical Respiration
Respiratory diseases
Biomedical and clinical sciences
convolutional neuron network (CNN)
Science
Q
Deep Learning-Assisted Sensitive 3C-SiC Sensor
thermal flow sensor
610
Deep learning
600
cubic silicon carbide (3C‐SiC)
0104 chemical sciences
T1-995
long‐term monitoring
respiration rate
Data structures and algorithms
0210 nano-technology
Biomedical engineering
Technology (General)
DOI:
10.1002/adsr.202300159
Publication Date:
2024-04-05T07:48:25Z
AUTHORS (10)
ABSTRACT
Abstract In human life, respiration serves as a crucial physiological signal. Continuous real‐time monitoring can provide valuable insights for the early detection and management of several respiratory diseases. High‐sensitivity, noninvasive, comfortable, long‐term stable devices are highly desirable. spite this, existing sensors cannot continuous due to erosion from moisture, fluctuations in body temperature, many other environmental factors. This research developed wearable thermal‐based sensor made cubic silicon carbide (3C‐SiC) using microfabrication process. The results showed that result Joule heating effect robustness 3C‐SiC material, offered high sensitivity with negative temperature coefficient resistance approximately 5,200ppmK ‐1 , an excellent response stability monitoring. Furthermore, by incorporating deep learning model, this fabricated develop advanced capabilities distinguish between four distinct breath patterns: slow, normal, fast, breathing, impressive classification accuracy rate ≈ 99.7%. represent significant step developing personal healthcare systems.
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