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Mobile Assisted Living: Smartwatch-based Fall Risk Assessment for Elderly People

 
: Haescher, Marian; Matthies, Denys J.C.; Srinivasan, Karthik; Bieber, Gerald

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Volltext urn:nbn:de:0011-n-5407071 (627 KByte PDF)
MD5 Fingerprint: f8a3c5b72cd9226082c6d621839cc61b
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Erstellt am: 13.4.2019


Matthies, D.J.C.; Haescher, M.; Yordanova, K.; Bieber, G.; Schröder, M.; Kirste, T.; Urban, B. ; 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-, Institutsteil Rostock:
iWOAR 2018, 5th international Workshop on Sensor-based Activity Recognition and Interaction. Proceedings : September 20-21, 2018, Berlin, Germany
New York: ACM, 2018 (ACM International Conference Proceeding Series)
ISBN: 978-1-4503-6487-4
Art. 6, 10 S.
International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR) <5, 2018, Rostock>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IGD ()
Fraunhofer IGD-R ()
Guiding Theme: Individual Health; Research Area: Human computer interaction (HCI); assistance; elderly user; health care; pattern recognition; smart watches

Abstract
We present a novel Smartwatch-based approach, to enable Mobile Assisted Living (MAL) for users with special needs. A major focus group for this approach are elderly people. We developed a tool for caregivers applicable in home environments, nursing care, and hospitals, to assess the vitality of their patients. Hereby, we particularly focus on the prediction of falls, because falls are a major reason for serious injuries and premature death among elderly. Therefore, we propose a multi parametric score based on standardized fall risk assessment tests, as well as on sleep quality, medication, patient history, motor skills, and environmental factors. The resulting total fall risk score reflects individual changes in behavior and vitality, which consequently enables for fall preventing interventions. Our system has been deployed and evaluated in a pilot study among 30 elderly patients over a period of four weeks.

: http://publica.fraunhofer.de/dokumente/N-540707.html