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Smartwatch based respiratory rate and breathing pattern recognition in an end-consumer environment

 
: Trimpop, John; Schenk, Hannes; Bieber, Gerald; Lämmel, Friedrich; Burggraf, Paul

:

Yordanova, Kristina (Ed.); Schröder, M.; Bader, S.; Kirste, Thomas ; Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Computer and Human Interaction -SIGCHI-; Fraunhofer-Institut für Graphische Datenverarbeitung -IGD-, Darmstadt:
iWOAR 2017, 4th International Workshop on Sensor-based Activity Recognition and Interaction : 21. - 22. September 2017, Rostock
New York: ACM Press, 2017 (ACM International Conference Proceedings Series 1183)
ISBN: 978-1-4503-5223-9
Art. 4, 5 S.
International Workshop on Sensor-based Activity Recognition (iWOAR) <4, 2017, Rostock>
Englisch
Konferenzbeitrag
Fraunhofer IGD ()
Fraunhofer IGD-R ()
mobile assistance; pattern recognition; sensor fusion; Guiding Theme: Individual Health; Research Area: Human computer interaction (HCI)

Abstract
Smartwatches as wearables became part of social life and practically and technically offer the possibility to collect medical body parameters next to usual fitness data. In this paper, we present an evaluation of the respiratory rate detection of the &gesund system. &gesund is a health assistance system, which automatically records detailed long-term health data with end-consumer smartwatches. The &gesund core is based on technology exclusively licensed from the Fraunhofer Institute of applied research. In our study, we compare the &gesund algorithms for respiration parameter detection in low-amplitude activities against data recorded from actual sleep laboratory patients. The results show accuracies of up to 89%. We are confident that wearable technologies will be used for medical health assistance in the near future.

: http://publica.fraunhofer.de/dokumente/N-477629.html