NDLI: Spectral Analysis of Accelerometry Signals From a Directed-Routine for Falls-Risk Estimation

Content Provider	IEEE Xplore Digital Library
Author	Ying Liu Redmond, S.J. Ning Wang Blumenkron, F. Narayanan, M.R. Lovell, N.H.
Copyright Year	1964
Abstract	Injurious falls are a prevalent and serious problem faced by a growing elderly population. Accurate assessment and long-term monitoring of falls-risk could prove useful in the prevention of falls, by identifying those at risk of falling early so targeted intervention may be prescribed. Previous studies have demonstrated the feasibility of using triaxial accelerometry to estimate the risk of a person falling in the near future, by characterizing their movement as they execute a restricted sequence of predefined movements in an unsupervised environment, termed a directed routine. This study presents an improvement on this previously published system, which relied explicitly on time-domain features extracted from the accelerometry signals. The proposed improvement incorporates features derived from spectral analysis of the same accelerometry signals; in particular the harmonic ratios between signal harmonics and the fundamental frequency component are used. Employing these additional frequency-domain features, in combination with the previously reported time-domain features, an increase in the observed correlation with the clinical gold-standard risk of falling, from ρ = 0.81 to ρ = 0.96, was achieved when using manually annotated event segmentation markers; using an automated algorithm to segment the signals gave corresponding results of ρ = 0.73 and ρ = 0.99, before and after the inclusion of spectral features. The strong correlation with falls-risk observed in this preliminary study further supports the feasibility of using an unsupervised assessment of falls-risk in the home environment.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	8
File Size	638523
Starting Page	2308
Ending Page	2315
File Format	PDF
ISSN	00189294
Volume Number	58
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Support vector machines Harmonic analysis Acceleration Time frequency analysis Correlation spectral analysis Accelerometry biosignal processing feature extraction falls-risk
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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