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Proceedings Paper

MEMS-based sensing and algorithm development for fall detection and gait analysis
Author(s): Piyush Gupta; Gabriel Ramirez; Donald Y. C. Lie; Tim Dallas; Ron E. Banister; Andrew Dentino
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Paper Abstract

Falls by the elderly are highly detrimental to health, frequently resulting in injury, high medical costs, and even death. Using a MEMS-based sensing system, algorithms are being developed for detecting falls and monitoring the gait of elderly and disabled persons. In this study, wireless sensors utilize Zigbee protocols were incorporated into planar shoe insoles and a waist mounted device. The insole contains four sensors to measure pressure applied by the foot. A MEMS based tri-axial accelerometer is embedded in the insert and a second one is utilized by the waist mounted device. The primary fall detection algorithm is derived from the waist accelerometer. The differential acceleration is calculated from samples received in 1.5s time intervals. This differential acceleration provides the quantification via an energy index. From this index one may ascertain different gait and identify fall events. Once a pre-determined index threshold is exceeded, the algorithm will classify an event as a fall or a stumble. The secondary algorithm is derived from frequency analysis techniques. The analysis consists of wavelet transforms conducted on the waist accelerometer data. The insole pressure data is then used to underline discrepancies in the transforms, providing more accurate data for classifying gait and/or detecting falls. The range of the transform amplitude in the fourth iteration of a Daubechies-6 transform was found sufficient to detect and classify fall events.

Paper Details

Date Published: 17 February 2010
PDF: 8 pages
Proc. SPIE 7593, Microfluidics, BioMEMS, and Medical Microsystems VIII, 75930U (17 February 2010); doi: 10.1117/12.841963
Show Author Affiliations
Piyush Gupta, Texas Tech Univ. (United States)
Gabriel Ramirez, Texas Tech Univ. (United States)
Donald Y. C. Lie, Texas Tech Univ. (United States)
Tim Dallas, Texas Tech Univ. (United States)
Ron E. Banister, Texas Tech Univ. (United States)
Andrew Dentino, Texas Tech Univ. (United States)

Published in SPIE Proceedings Vol. 7593:
Microfluidics, BioMEMS, and Medical Microsystems VIII
Holger Becker; Wanjun Wang, Editor(s)

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