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Dynamic Bayesian networks for context-aware fall risk assessment
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-0712-6015
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1940-1747
Örebro University.
2014 (English)In: Sensors (Switzerland), ISSN 1424-8220, Vol. 14, no 5, p. 9330-9348Article in journal (Refereed) Published
Abstract [en]

Fall incidents among the elderly often occur in the home and can cause serious injuries affecting their independent living. This paper presents an approach where data from wearable sensors integrated in a smart home environment is combined using a dynamic Bayesian network. The smart home environment provides contextual data, obtained from environmental sensors, and contributes to assessing a fall risk probability. The evaluation of the developed system is performed through simulation. Each time step is represented by a single user activity and interacts with a fall sensors located on a mobile device. A posterior probability is calculated for each recognized activity or contextual information. The output of the system provides a total risk assessment of falling given a response from the fall sensor.

Place, publisher, year, edition, pages
2014. Vol. 14, no 5, p. 9330-9348
Keyword [en]
Ambient assisted living (AAL), Context recognition, Dynamic Bayesian networks (DBN), Fall detection, Multi-sensor fusion
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-25229DOI: 10.3390/s140509330ISI: 000337112200090Scopus ID: 2-s2.0-84901335545OAI: oai:DiVA.org:mdh-25229DiVA, id: diva2:724764
Available from: 2014-06-13 Created: 2014-06-13 Last updated: 2018-02-23Bibliographically approved

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Koshmak, GregoryLindén, Maria

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Citation style
  • apa
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  • de-DE
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Output format
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  • asciidoc
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