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Comparison of low-complexity fall detection algorithms for body attached accelerometers
Department of Medical Technology, University of Oulu.
Department of Medical Technology, University of Oulu.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
Department of Medical Technology, University of Oulu.
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2008 (English)In: Gait & Posture, ISSN 0966-6362, E-ISSN 1879-2219, Vol. 28, no 3, p. 285-291Article in journal (Refereed) Published
Abstract [en]

The elderly population is growing rapidly. Fall related injuries are a central problem for this population. Elderly people desire to live at home, and thus, new technologies, such as automated fall detectors, are needed to support their independence and security. The aim of this study was to evaluate different low-complexity fall detection algorithms, using triaxial accelerometers attached at the waist, wrist, and head. The fall data were obtained from standardized types of intentional falls (forward, backward, and lateral) in three middle-aged subjects. Data from activities of daily living were used as reference. Three different detection algorithms with increasing complexity were investigated using two or more of the following phases of a fall event: beginning of the fall, falling velocity, fall impact, and posture after the fall. The results indicated that fall detection using a triaxial accelerometer worn at the waist or head is efficient, even with quite simple threshold-based algorithms, with a sensitivity of 97-98% and specificity of 100%. The most sensitive acceleration parameters in these algorithms appeared to be the resultant signal with no high-pass filtering, and the calculated vertical acceleration. In this study, the wrist did not appear to be an applicable site for fall detection. Since a head worn device includes limitations concerning usability and acceptance, a waist worn accelerometer, using an algorithm that recognizes the impact and the posture after the fall, might be optimal for fall detection.

Place, publisher, year, edition, pages
2008. Vol. 28, no 3, p. 285-291
National Category
Embedded Systems
Research subject
Embedded System
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URN: urn:nbn:se:ltu:diva-8830DOI: 10.1016/j.gaitpost.2008.01.003ISI: 000258249500016Scopus ID: 2-s2.0-46149107931Local ID: 76092aa0-7337-11dc-86ab-000ea68e967bOAI: oai:DiVA.org:ltu-8830DiVA, id: diva2:981768
Note
Validerad; 2008; 20071005 (pln)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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