NDLI: Assessing elderly persons' fall risk using spectral analysis on accelerometric data

Content Provider	IEEE Xplore Digital Library
Author	Marschollek, Michael Wolf, Klaus-Hendrik Gietzelt, Matthias Nemitz, Gerhard Meyer zu Schwabedissen, Hubertus Haux, Reinhold
Copyright Year	2008
Description	Author affiliation: Department for Geriatrics at the Braunschweig Medical Center, Germany (Nemitz, Gerhard; Meyer zu Schwabedissen, Hubertus) \|\| Institute for Medical Informatics of the University of Braunschweig - Institute of Technology and Medical School Hannover, Muehlenpfordtstrasse 23, 38106 Braunschweig, Germany (Marschollek, Michael; Wolf, Klaus-Hendrik; Gietzelt, Matthias; Haux, Reinhold)
Abstract	Falls are among the leading causes for morbidity, mortality and lasting functional disability in the elderly population. Several studies have shown the applicability of accelerometry to detect persons with a high fall risk. Most of these studies have been conducted under laboratory settings and without clear definition of ‘fall risk’ reference measures. The aim of our work is to provide a simple unsupervised method to assess the fall risk of elderly persons as measured by reference clinical fall risk assessment scores. Our method uses parameters computed by spectral analysis on triaxial accelerometer data recorded in a clinical setting, and is evaluated using simple logistic regression classifier models with reference to three clinical reference scores. The overall prediction accuracy of the models ranges from 65.5–89.1%, with sensitivity and specificity between 78.5–99% and 15.4–60.4%, respectively. Our results show that our simple method can be used to detect persons with a high fall risk with a fair to good predictive accuracy when tested against common clinical reference scores. Our parameters are independent of specific test procedures and therefore are suited for use in an unsupervised setting. Our future research will include the evaluation of our method in a large prospective study.
Starting Page	3682
Ending Page	3685
File Size	221036
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4650008
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	geriatrics wearable sensors fall risk classification elderly people home monitoring machine learning
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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