NDLI: A performance comparison of accelerometry-based step detection algorithms on a large, non-laboratory sample of healthy and mobility-impaired persons

Content Provider	IEEE Xplore Digital Library
Author	Marschollek, Michael Goevercin, Mehmet Wolf, Klaus-Hendrik Song, Bianying Gietzelt, Matthias Haux, Reinhold Steinhagen-Thiessen, Elisabeth
Copyright Year	2008
Description	Author affiliation: Department for Geriatric Medicine at the Charité, Berlin (Germany) (Goevercin, Mehmet; Steinhagen-Thiessen, Elisabeth) \|\| Peter L. Reichertz Institute for Medical Informatics of the University of Braunschweig - Institute of Technology and Medical School Hannover, Muehlenpfordtstrasse 23, 38106, Germany (Marschollek, Michael; Wolf, Klaus-Hendrik; Song, Bianying; Gietzelt, Matthias; Haux, Reinhold)
Abstract	Accelerometers are frequently used for activity assessment and as reference devices for counting steps. Their performance on healthy subjects' data is good, but there are doubts as to their applicability on elderly and mobility-impaired subjects. Furthermore, only few step detection algorithms have been published so far, and their performance has not been evaluated on a large, non-laboratory sample. The aim of this paper is to compare the performance of four freely accessible accelerometry-based step detection algorithms in a non-laboratory setting. Two samples of healthy persons (n=140) and mobility-impaired, geriatric in-patients (n=10) wore a single triaxial accelerometer on a waist-belt during unconstrained walking. The relative error rate of the four algorithms on the two samples was compared with reference video recordings. All four algorithms show a fairly poor performance on healthy subjects' (8.4–30.8% relative error rate) and especially geriatric patients' data (28.1–62.1%). Among the tested ones, a simple autocorrelation algorithm works best on both data sets together. More complex algorithms might work better, and more research is needed to evaluate the accuracy of step detection methods on mobility-impaired subjects.
Starting Page	1319
Ending Page	1322
File Size	238070
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4649407
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	triaxial accelerometry wearable sensors step detection elderly people home monitoring
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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