Automatic identification of gait events using an instrumented sock

Background 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...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Preece, Stephen J [verfasserIn] Kenney, Laurence PJ Major, Matthew J Dias, Tilak Lay, Edward Fernandes, Bosco T

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Sensor Signal Gait Cycle Functional Electrical Stimulation Sensor Output Heel Strike

Anmerkung:	© Preece et al; licensee BioMed Central Ltd. 2011

Übergeordnetes Werk:	Enthalten in: Journal of neuroEngineering and rehabilitation - London : BioMed Central, 2004, 8(2011), 1 vom: 27. Mai
Übergeordnetes Werk:	volume:8 ; year:2011 ; number:1 ; day:27 ; month:05

Links:	Volltext

DOI / URN:	10.1186/1743-0003-8-32

Katalog-ID:	SPR02921906X

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520			\|a Background 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. Methods 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. Results 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. Conclusions This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance.
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allfields	10.1186/1743-0003-8-32 doi (DE-627)SPR02921906X (SPR)1743-0003-8-32-e DE-627 ger DE-627 rakwb eng Preece, Stephen J verfasserin aut Automatic identification of gait events using an instrumented sock 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Preece et al; licensee BioMed Central Ltd. 2011 Background 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. Methods 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. Results 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. Conclusions This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance. Sensor Signal (dpeaa)DE-He213 Gait Cycle (dpeaa)DE-He213 Functional Electrical Stimulation (dpeaa)DE-He213 Sensor Output (dpeaa)DE-He213 Heel Strike (dpeaa)DE-He213 Kenney, Laurence PJ aut Major, Matthew J aut Dias, Tilak aut Lay, Edward aut Fernandes, Bosco T aut Enthalten in Journal of neuroEngineering and rehabilitation London : BioMed Central, 2004 8(2011), 1 vom: 27. Mai (DE-627)461907933 (DE-600)2164377-5 1743-0003 nnns volume:8 year:2011 number:1 day:27 month:05 https://dx.doi.org/10.1186/1743-0003-8-32 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2011 1 27 05
spelling	10.1186/1743-0003-8-32 doi (DE-627)SPR02921906X (SPR)1743-0003-8-32-e DE-627 ger DE-627 rakwb eng Preece, Stephen J verfasserin aut Automatic identification of gait events using an instrumented sock 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Preece et al; licensee BioMed Central Ltd. 2011 Background 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. Methods 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. Results 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. Conclusions This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance. Sensor Signal (dpeaa)DE-He213 Gait Cycle (dpeaa)DE-He213 Functional Electrical Stimulation (dpeaa)DE-He213 Sensor Output (dpeaa)DE-He213 Heel Strike (dpeaa)DE-He213 Kenney, Laurence PJ aut Major, Matthew J aut Dias, Tilak aut Lay, Edward aut Fernandes, Bosco T aut Enthalten in Journal of neuroEngineering and rehabilitation London : BioMed Central, 2004 8(2011), 1 vom: 27. Mai (DE-627)461907933 (DE-600)2164377-5 1743-0003 nnns volume:8 year:2011 number:1 day:27 month:05 https://dx.doi.org/10.1186/1743-0003-8-32 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2011 1 27 05
allfields_unstemmed	10.1186/1743-0003-8-32 doi (DE-627)SPR02921906X (SPR)1743-0003-8-32-e DE-627 ger DE-627 rakwb eng Preece, Stephen J verfasserin aut Automatic identification of gait events using an instrumented sock 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Preece et al; licensee BioMed Central Ltd. 2011 Background 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. Methods 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. Results 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. Conclusions This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance. Sensor Signal (dpeaa)DE-He213 Gait Cycle (dpeaa)DE-He213 Functional Electrical Stimulation (dpeaa)DE-He213 Sensor Output (dpeaa)DE-He213 Heel Strike (dpeaa)DE-He213 Kenney, Laurence PJ aut Major, Matthew J aut Dias, Tilak aut Lay, Edward aut Fernandes, Bosco T aut Enthalten in Journal of neuroEngineering and rehabilitation London : BioMed Central, 2004 8(2011), 1 vom: 27. Mai (DE-627)461907933 (DE-600)2164377-5 1743-0003 nnns volume:8 year:2011 number:1 day:27 month:05 https://dx.doi.org/10.1186/1743-0003-8-32 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2011 1 27 05
allfieldsGer	10.1186/1743-0003-8-32 doi (DE-627)SPR02921906X (SPR)1743-0003-8-32-e DE-627 ger DE-627 rakwb eng Preece, Stephen J verfasserin aut Automatic identification of gait events using an instrumented sock 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Preece et al; licensee BioMed Central Ltd. 2011 Background 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. Methods 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. Results 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. Conclusions This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance. Sensor Signal (dpeaa)DE-He213 Gait Cycle (dpeaa)DE-He213 Functional Electrical Stimulation (dpeaa)DE-He213 Sensor Output (dpeaa)DE-He213 Heel Strike (dpeaa)DE-He213 Kenney, Laurence PJ aut Major, Matthew J aut Dias, Tilak aut Lay, Edward aut Fernandes, Bosco T aut Enthalten in Journal of neuroEngineering and rehabilitation London : BioMed Central, 2004 8(2011), 1 vom: 27. Mai (DE-627)461907933 (DE-600)2164377-5 1743-0003 nnns volume:8 year:2011 number:1 day:27 month:05 https://dx.doi.org/10.1186/1743-0003-8-32 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2011 1 27 05
allfieldsSound	10.1186/1743-0003-8-32 doi (DE-627)SPR02921906X (SPR)1743-0003-8-32-e DE-627 ger DE-627 rakwb eng Preece, Stephen J verfasserin aut Automatic identification of gait events using an instrumented sock 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Preece et al; licensee BioMed Central Ltd. 2011 Background 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. Methods 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. Results 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. Conclusions This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance. Sensor Signal (dpeaa)DE-He213 Gait Cycle (dpeaa)DE-He213 Functional Electrical Stimulation (dpeaa)DE-He213 Sensor Output (dpeaa)DE-He213 Heel Strike (dpeaa)DE-He213 Kenney, Laurence PJ aut Major, Matthew J aut Dias, Tilak aut Lay, Edward aut Fernandes, Bosco T aut Enthalten in Journal of neuroEngineering and rehabilitation London : BioMed Central, 2004 8(2011), 1 vom: 27. Mai (DE-627)461907933 (DE-600)2164377-5 1743-0003 nnns volume:8 year:2011 number:1 day:27 month:05 https://dx.doi.org/10.1186/1743-0003-8-32 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2011 1 27 05
language	English
source	Enthalten in Journal of neuroEngineering and rehabilitation 8(2011), 1 vom: 27. Mai volume:8 year:2011 number:1 day:27 month:05
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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. Methods 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. Results 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. Conclusions This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensor Signal</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gait Cycle</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Functional Electrical Stimulation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensor Output</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Heel Strike</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kenney, Laurence PJ</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Major, Matthew J</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dias, Tilak</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lay, Edward</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fernandes, Bosco T</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of neuroEngineering and rehabilitation</subfield><subfield code="d">London : BioMed Central, 2004</subfield><subfield code="g">8(2011), 1 vom: 27. 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author	Preece, Stephen J
spellingShingle	Preece, Stephen J misc Sensor Signal misc Gait Cycle misc Functional Electrical Stimulation misc Sensor Output misc Heel Strike Automatic identification of gait events using an instrumented sock
authorStr	Preece, Stephen J
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topic_title	Automatic identification of gait events using an instrumented sock Sensor Signal (dpeaa)DE-He213 Gait Cycle (dpeaa)DE-He213 Functional Electrical Stimulation (dpeaa)DE-He213 Sensor Output (dpeaa)DE-He213 Heel Strike (dpeaa)DE-He213
topic	misc Sensor Signal misc Gait Cycle misc Functional Electrical Stimulation misc Sensor Output misc Heel Strike
topic_unstemmed	misc Sensor Signal misc Gait Cycle misc Functional Electrical Stimulation misc Sensor Output misc Heel Strike
topic_browse	misc Sensor Signal misc Gait Cycle misc Functional Electrical Stimulation misc Sensor Output misc Heel Strike
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Automatic identification of gait events using an instrumented sock
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title_full	Automatic identification of gait events using an instrumented sock
author_sort	Preece, Stephen J
journal	Journal of neuroEngineering and rehabilitation
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author_browse	Preece, Stephen J Kenney, Laurence PJ Major, Matthew J Dias, Tilak Lay, Edward Fernandes, Bosco T
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author-letter	Preece, Stephen J
doi_str_mv	10.1186/1743-0003-8-32
title_sort	automatic identification of gait events using an instrumented sock
title_auth	Automatic identification of gait events using an instrumented sock
abstract	Background 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. Methods 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. Results 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. Conclusions This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance. © Preece et al; licensee BioMed Central Ltd. 2011
abstractGer	Background 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. Methods 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. Results 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. Conclusions This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance. © Preece et al; licensee BioMed Central Ltd. 2011
abstract_unstemmed	Background 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. Methods 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. Results 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. Conclusions This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance. © Preece et al; licensee BioMed Central Ltd. 2011
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
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title_short	Automatic identification of gait events using an instrumented sock
url	https://dx.doi.org/10.1186/1743-0003-8-32
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author2	Kenney, Laurence PJ Major, Matthew J Dias, Tilak Lay, Edward Fernandes, Bosco T
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up_date	2024-07-04T00:01:16.861Z
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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. Methods 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. Results 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. 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Automatic identification of gait events using an instrumented sock

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