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Proceedings Paper

Fall risks assessment among community dwelling elderly using wearable wireless sensors
Author(s): Thurmon E. Lockhart; Rahul Soangra; Chris Frames
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Paper Abstract

Postural stability characteristics are considered to be important in maintaining functional independence free of falls and healthy life style especially for the growing elderly population. This study focuses on developing tools of clinical value in fall prevention: 1) Implementation of sensors that are minimally obtrusive and reliably record movement data. 2) Unobtrusively gather data from wearable sensors from four community centers 3) developed and implemented linear and non-linear signal analysis algorithms to extract clinically relevant information using wearable technology. In all a total of 100 community dwelling elderly individuals (66 non-fallers and 34 fallers) participated in the experiment. All participants were asked to stand-still in eyes open (EO) and eyes closed (EC) condition on forceplate with one wireless inertial sensor affixed at sternum level. Participants’ history of falls had been recorded for last 2 years, with emphasis on frequency and characteristics of falls. Any participant with at least one fall in the prior year were classified as faller and the others as non-faller. The results indicated several key factors/features of postural characteristics relevant to balance control and stability during quite stance and, showed good predictive capability of fall risks among older adults. Wearable technology allowed us to gather data where it matters the most to answer fall related questions, i.e. the community setting environments. This study opens new prospects of clinical testing using postural variables with a wearable sensor that may be relevant for assessing fall risks at home and patient environment in near future.

Paper Details

Date Published: 20 June 2014
PDF: 7 pages
Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 90911J (20 June 2014); doi: 10.1117/12.2050995
Show Author Affiliations
Thurmon E. Lockhart, Virginia Polytechnic Institute and State Univ. (United States)
Virginia Tech-Wake Forest Univ. (United States)
Rahul Soangra, Virginia Tech-Wake Forest Univ. (United States)
Chris Frames, Virginia Polytechnic Institute and State Univ. (United States)

Published in SPIE Proceedings Vol. 9091:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII
Ivan Kadar, Editor(s)

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