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Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment

School of Innovation, Design and Engineering, Mälardalen University, Högskoleplan 1, Västerås 721 23, Sweden
Center for Applied Autonomous Sensor Systems (AASS), Örebro University, Fakultetsgatan 1, Örebro 701 82, Sweden
Author to whom correspondence should be addressed.
Sensors 2014, 14(5), 9330-9348;
Received: 19 March 2014 / Revised: 12 May 2014 / Accepted: 21 May 2014 / Published: 23 May 2014
PDF [468 KB, uploaded 21 June 2014]


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. View Full-Text
Keywords: ambient assisted living (AAL); fall detection; context recognition; multi-sensor fusion; dynamic Bayesian networks (DBN) ambient assisted living (AAL); fall detection; context recognition; multi-sensor fusion; dynamic Bayesian networks (DBN)

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Koshmak, G.; Linden, M.; Loutfi, A. Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment. Sensors 2014, 14, 9330-9348.

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