Skip to main navigation Skip to search Skip to main content

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

Jethro Vernon, Pep Canyelles-Pericas, Hamdi Torun, Richard Binns, Wai Pang Ng, Qiang Wu, Yongqing (Richard) Fu*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)
    122 Downloads (Pure)

    Abstract

    Apnoea, a major sleep disorder, has affected many adults and caused 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 treatments, 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 Acousto-Pi open-source Surface Acoustic Wave (SAW) platform (Apnoea-Pi), to monitor and recognise apnoea in patients. The platform is based on thin film SAW, using bimorph ZnO and aluminium structures, including those fabricated in Al foils or plates, to achieve for breath tracking based on the 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 thin film SAW devices out-perform standard and off-the-shelf capacitive electronic sensors regarding to their responses 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.
    Original languageEnglish
    Article number033002
    Number of pages10
    JournalNanotechnology and Precision Engineering
    Volume5
    Issue number3
    Early online date18 Aug 2022
    DOIs
    Publication statusPublished - 1 Sept 2022

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    Keywords

    • Surface acoustic waves
    • sleep disorder
    • apnoea
    • open-source electronics
    • pattern recognition
    • piezoelectric thin film

    Fingerprint

    Dive into the research topics of 'Breath monitoring, sleep disorder detection and tracking using thin film acoustic waves and open-source electronics'. Together they form a unique fingerprint.

    Cite this