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Unobtrusive Respiratory Rate Detection within Homecare Scenarios

    Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

    Abstract

    Against the background of Ambient Assisted Living, this article proposes a new kind of unobtrusive, non-stigmatizing and continuous acquisition of vital signs as respiratory rate and related features on the basis of ultra-wide-band radar sensing. Through the runtime analysis of the reflection data, surrogating mechanical signals e.g. the excursion of the thorax are detected and linked with physiological values like the breathing rate. After a brief introduction to the application context including an explanation of specific user demands and restrictions of current solutions of the telemedicine, physical fundamentals of measurement and the utilized electronics, the applied principles of spatial and temporal data mining are described. Finally, experiments including real measurements with the subsequent discussion of the measurement results provide an outlook to the capabilities of our approach and grant information about open issues and the steps in research.
    Translated title of the contributionUnaufdringliche Erfassung von Atemraten in Heimpflegeszenarien
    Original languageEnglish
    Title of host publicationAmbient Assisted Living : 6. AAL-Kongress 2013 Berlin, Germany, January 22. - 23. , 2013
    EditorsReiner Wichert, Helmut Klausing
    Number of pages18
    PublisherSpringer Verlag
    Publication date2014
    Pages61-78
    ISBN (Print)978-3-642-37987-1
    ISBN (Electronic)978-3-642-37988-8
    DOIs
    Publication statusPublished - 2014
    Event6. AAL-Kongress: Lebensqualität im Wandel von Demografie und Technik - Berlin, Germany
    Duration: 22.01.201323.01.2013
    Conference number: 6
    https://conference.vde.com/aal/rueckblick/rb6/Seiten/default.aspx

    Research areas and keywords

    • Informatics
    • Reflection Pattern
    • Soft Thresholding
    • Nasal Prong
    • Fast Wavelet Transform
    • Temporal Data Mining

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