Breath monitoring, sleep disorder detection, and tracking using thin-film acoustic waves and open-source electronics

IF 3.5 3区 工程技术 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Nami Jishu yu Jingmi Gongcheng/Nanotechnology and Precision Engineering Pub Date : 2022-09-01 DOI:10.1063/10.0013471
Jethro Vernon, P. Canyelles-Pericas, H. Torun, R. Binns, Wai Pang Ng, Qiang Wu, Y. Fu
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引用次数: 2

Abstract

Apnoea, a major sleep disorder, affects many adults and causes several issues, such as fatigue, high blood pressure, liver conditions, increased risk of type II diabetes, and heart problems. Therefore, advanced monitoring and diagnosing tools of apnoea disorders are needed to facilitate better treatment, with advantages such as accuracy, comfort of use, cost effectiveness, and embedded computation capabilities to recognise, store, process, and transmit time series data. In this work we present an adaptation of our apnoea-Pi open-source surface acoustic wave (SAW) platform (Apnoea-Pi) to monitor and recognise apnoea in patients. The platform is based on a thin-film SAW device using bimorph ZnO and Al structures, including those fabricated as Al foils or plates, to achieve breath tracking based on humidity and temperature changes. We applied open-source electronics and provided embedded computing characteristics for signal processing, data recognition, storage, and transmission of breath signals. We show that the thin-film SAW device out-performed standard and off-the-shelf capacitive electronic sensors in terms of their response and accuracy for human breath-tracking purposes. This in combination with embedded electronics makes a suitable platform for human breath monitoring and sleep disorder recognition.
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使用薄膜声波和开源电子设备进行呼吸监测、睡眠障碍检测和跟踪
呼吸暂停是一种主要的睡眠障碍,影响到许多成年人,并导致许多问题,如疲劳、高血压、肝脏疾病、II型糖尿病风险增加和心脏病。因此,需要先进的呼吸暂停障碍监测和诊断工具,以促进更好的治疗,这些工具具有准确性、使用舒适、成本效益和嵌入式计算能力,可以识别、存储、处理和传输时间序列数据。在这项工作中,我们提出了我们的apnoea- pi开源表面声波(SAW)平台(apnoea- pi)来监测和识别患者的呼吸暂停。该平台基于薄膜SAW器件,采用双晶型ZnO和Al结构,包括那些制成Al箔或板的结构,以实现基于湿度和温度变化的呼吸跟踪。我们应用了开源电子技术,并为信号处理、数据识别、存储和传输呼吸信号提供了嵌入式计算特性。我们表明,薄膜SAW器件在人体呼吸跟踪目的的响应和准确性方面优于标准和现成的电容式电子传感器。这与嵌入式电子设备相结合,为人类呼吸监测和睡眠障碍识别提供了合适的平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nami Jishu yu Jingmi Gongcheng/Nanotechnology and Precision Engineering
Nami Jishu yu Jingmi Gongcheng/Nanotechnology and Precision Engineering Engineering-Industrial and Manufacturing Engineering
CiteScore
6.50
自引率
0.00%
发文量
1379
审稿时长
14 weeks
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