Automatic identification of gait events using an instrumented sock

<p>Abstract</p> <p>Background</p> <p>Textile-based transducers are an emerging technology in which piezo-resistive properties of materials are used to measure an applied strain. By incorporating these sensors into a sock, this technology offers the potential to detect c...

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Main Authors: Dias Tilak, Major Matthew J, Kenney Laurence PJ, Preece Stephen J, Lay Edward, Fernandes Bosco T
Format: Article
Language:English
Published: BMC 2011-05-01
Series:Journal of NeuroEngineering and Rehabilitation
Online Access:http://www.jneuroengrehab.com/content/8/1/32
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spelling doaj-edd2b0a6cef54920b8e655dc8ee6813a2020-11-24T21:53:01ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032011-05-01813210.1186/1743-0003-8-32Automatic identification of gait events using an instrumented sockDias TilakMajor Matthew JKenney Laurence PJPreece Stephen JLay EdwardFernandes Bosco T<p>Abstract</p> <p>Background</p> <p>Textile-based transducers are an emerging technology in which piezo-resistive properties of materials are used to measure an applied strain. By incorporating these sensors into a sock, this technology offers the potential to detect critical events during the stance phase of the gait cycle. This could prove useful in several applications, such as functional electrical stimulation (FES) systems to assist gait.</p> <p>Methods</p> <p>We investigated the output of a knitted resistive strain sensor during walking and sought to determine the degree of similarity between the sensor output and the ankle angle in the sagittal plane. In addition, we investigated whether it would be possible to predict three key gait events, heel strike, heel lift and toe off, with a relatively straight-forward algorithm. This worked by predicting gait events to occur at fixed time offsets from specific peaks in the sensor signal.</p> <p>Results</p> <p>Our results showed that, for all subjects, the sensor output exhibited the same general characteristics as the ankle joint angle. However, there were large between-subjects differences in the degree of similarity between the two curves. Despite this variability, it was possible to accurately predict gait events using a simple algorithm. This algorithm displayed high levels of trial-to-trial repeatability.</p> <p>Conclusions</p> <p>This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance.</p> http://www.jneuroengrehab.com/content/8/1/32
collection DOAJ
language English
format Article
sources DOAJ
author Dias Tilak
Major Matthew J
Kenney Laurence PJ
Preece Stephen J
Lay Edward
Fernandes Bosco T
spellingShingle Dias Tilak
Major Matthew J
Kenney Laurence PJ
Preece Stephen J
Lay Edward
Fernandes Bosco T
Automatic identification of gait events using an instrumented sock
Journal of NeuroEngineering and Rehabilitation
author_facet Dias Tilak
Major Matthew J
Kenney Laurence PJ
Preece Stephen J
Lay Edward
Fernandes Bosco T
author_sort Dias Tilak
title Automatic identification of gait events using an instrumented sock
title_short Automatic identification of gait events using an instrumented sock
title_full Automatic identification of gait events using an instrumented sock
title_fullStr Automatic identification of gait events using an instrumented sock
title_full_unstemmed Automatic identification of gait events using an instrumented sock
title_sort automatic identification of gait events using an instrumented sock
publisher BMC
series Journal of NeuroEngineering and Rehabilitation
issn 1743-0003
publishDate 2011-05-01
description <p>Abstract</p> <p>Background</p> <p>Textile-based transducers are an emerging technology in which piezo-resistive properties of materials are used to measure an applied strain. By incorporating these sensors into a sock, this technology offers the potential to detect critical events during the stance phase of the gait cycle. This could prove useful in several applications, such as functional electrical stimulation (FES) systems to assist gait.</p> <p>Methods</p> <p>We investigated the output of a knitted resistive strain sensor during walking and sought to determine the degree of similarity between the sensor output and the ankle angle in the sagittal plane. In addition, we investigated whether it would be possible to predict three key gait events, heel strike, heel lift and toe off, with a relatively straight-forward algorithm. This worked by predicting gait events to occur at fixed time offsets from specific peaks in the sensor signal.</p> <p>Results</p> <p>Our results showed that, for all subjects, the sensor output exhibited the same general characteristics as the ankle joint angle. However, there were large between-subjects differences in the degree of similarity between the two curves. Despite this variability, it was possible to accurately predict gait events using a simple algorithm. This algorithm displayed high levels of trial-to-trial repeatability.</p> <p>Conclusions</p> <p>This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance.</p>
url http://www.jneuroengrehab.com/content/8/1/32
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