Document Detail

Real-time gait event detection using wearable sensors.
MedLine Citation:
PMID:  19729307     Owner:  NLM     Status:  MEDLINE    
Real-time gait event detection is a requirement for functional electrical stimulation and gait biofeedback. This gait event detection should ideally be achieved using an ambulatory system of durable, lightweight, low-cost sensors. Previous research has reported issues with durability in footswitch systems. Therefore, this study describes the development and assessment of novel detection algorithms using footswitch and accelerometer sensors on 12 healthy individuals. Subjects were equipped with one force sensitive resistor on the heel, one accelerometer at the foot, and one accelerometer at the knee. Subjects performed 10, 8-m walking trials in each of three conditions: normal, slow, and altered (reduced knee ROM) walking. Data from a subset of four subjects were used to develop prediction algorithms for initial contact (IC). Subsequently, these algorithms were tested on the remaining eight subjects against standard forceplate IC data (threshold of 5 N on a rising edge). The footswitch force threshold algorithm was most accurate for IC detection (mean absolute error of 2.4+/-2.1 ms) and was significantly more accurate (p<0.001) than the optimal accelerometer algorithm (mean absolute error of 9.5+/-9.0 ms). The optimal accelerometer algorithm used data from both accelerometers, with IC determined from the second derivative of foot fore-aft acceleration. The error results for footswitch and accelerometer algorithms are lower (approximately 60%) than in previous research on ambulatory real-time gait event detection systems. Currently, footswitch systems must be recommended over accelerometer systems for accurate detection of IC, however, further research into accelerometer algorithms is merited due to its advantages as a durable, low-cost sensor.
Michael Hanlon; Ross Anderson
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2009-09-03
Journal Detail:
Title:  Gait & posture     Volume:  30     ISSN:  1879-2219     ISO Abbreviation:  Gait Posture     Publication Date:  2009 Nov 
Date Detail:
Created Date:  2009-09-28     Completed Date:  2010-01-07     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9416830     Medline TA:  Gait Posture     Country:  England    
Other Details:
Languages:  eng     Pagination:  523-7     Citation Subset:  IM    
Sport and Exercise Sciences Research Institute, University of Ulster, School of Sports Studies, Jordanstown, Co. Antrim, Northern Ireland.
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MeSH Terms
Biofeedback, Psychology
Electric Stimulation
Gait / physiology*
Monitoring, Physiologic / instrumentation*
Orthotic Devices
Signal Processing, Computer-Assisted
Young Adult

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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