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Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment
Mälardalen Univ, Sch Innovat Design & Engn, Västerås, Sweden.
Mälardalen Univ, Sch Innovat Design & Engn, Västerås, Sweden.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-3122-693X
2014 (English)In: Sensors, ISSN 1424-8220, Vol. 14, no 5, 9330-9348 p.Article 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
Basel: MDPI AG , 2014. Vol. 14, no 5, 9330-9348 p.
Keyword [en]
ambient assisted living (AAL), fall detection, context recognition, multi-sensor fusion, dynamic Bayesian networks (DBN)
National Category
Computer Science
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-35829DOI: 10.3390/s140509330ISI: 000337112200090OAI: oai:DiVA.org:oru-35829DiVA: diva2:740518
Available from: 2014-08-25 Created: 2014-07-30 Last updated: 2017-10-17Bibliographically approved

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