Deep Learning-Assisted Sensitive 3C-SiC Sensor for Long-Term Monitoring of Physical Respiration

Thi Lap Tran, Duy Van Nguyen, Hung Nguyen, Thi Phuoc Van Nguyen, Pingan Song, Ravinesh C Deo, Clint Moloney, Viet Dung Dao, Nam-Trung Nguyen, Toan Dinh
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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-SiC material, 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.

Abstract Image

深度学习辅助灵敏 3C-SiC 传感器用于长期监测生理呼吸
在人类生活中,呼吸是一个重要的生理信号。连续实时的呼吸监测可为多种呼吸系统疾病的早期检测和治疗提供有价值的信息。高灵敏度、无创、舒适和长期稳定的呼吸设备非常受欢迎。尽管如此,由于湿度、体温波动和许多其他环境因素的侵蚀,现有的呼吸传感器无法提供连续的长期监测。这项研究利用微加工工艺开发了一种由立方碳化硅(3C-SiC)制成的可穿戴热式呼吸传感器。结果表明,由于坚固的 3C-SiC 材料中存在焦耳加热效应,该传感器具有高灵敏度,其电阻负温度系数约为 5200ppmK-1,对呼吸和长期稳定性监测具有出色的响应能力。此外,通过结合深度学习模型,该制备的传感器可以开发出先进的功能,以区分四种不同的呼吸模式:缓慢呼吸、正常呼吸、快速呼吸和深呼吸,分类准确率高达 ≈ 99.7%。这项研究成果标志着为个人医疗系统开发可穿戴呼吸传感器迈出了重要一步。
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