title: Dynamic Bayesian networks for context-aware fall risk assessment.
abstract: 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.
authors.affiliation: School of Innovation, Design and Engineering, Mälardalen University, Högskoleplan 1, Västerås 721 23, Sweden. gregory.koshmak@mdh.se.
authors.forename: Gregory
authors.initials: G
authors.lastname: Koshmak
authors.affiliation: School of Innovation, Design and Engineering, Mälardalen University, Högskoleplan 1, Västerås 721 23, Sweden. maria.linden@mdh.se.
authors.forename: Maria
authors.initials: M
authors.lastname: Linden
authors.affiliation: Center for Applied Autonomous Sensor Systems (AASS), Örebro University, Fakultetsgatan 1, Örebro 701 82, Sweden. amy.loutfi@oru.se.
authors.forename: Amy
authors.initials: A
authors.lastname: Loutfi
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; D056044:Actigraphy / Q000295:instrumentation* / Q000379:methods; D000465:Algorithms; D001499:Bayes Theorem; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity