A Force Myography-Based System for Gait Event Detection in Overground and Ramp Walking
In this paper, we present a novel method to determine the heel strike (HS) and toe-off (TO) during overground (OG) and ramp walking, including the transition. The method utilizes force myography (FMG) signals from thighs while subjects walked on OG and ramp. Five adult male subjects wore a wireless...
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Veröffentlicht in: | IEEE transactions on instrumentation and measurement 2018-10, Vol.67 (10), p.2314-2323 |
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description | In this paper, we present a novel method to determine the heel strike (HS) and toe-off (TO) during overground (OG) and ramp walking, including the transition. The method utilizes force myography (FMG) signals from thighs while subjects walked on OG and ramp. Five adult male subjects wore a wireless FMG data acquisition system, developed in-house using force resistive sensors and electronic components. A heuristic approach for subject-dependent and terrain-independent model was developed to determine HS and TO in a given gait cycle in steady state and transition. The average error in HS determination was 9.66 ± 8.29, 9.38 ± 9.35, and 13.94 ± 18.95 ms, while TO was determined with an average error of 16.99 ± 18.12, 13.35 ± 15.10, and 17.29 ± 21.92 ms for OG, ramp, and transition, respectively. The proposed system is less expensive, simple to develop, and friendly to wear. The reported errors are comparable to previously reported errors using pressure sensitive insole, gyroscope, accelerometers, and electromyography, which are much complex and expensive in comparison to proposed FMG-based system. Although the tests were conducted on healthy subjects, the system promises to be generalizable to amputee and other pathological gaits also. While the tests were conducted on young adults at self-selected speeds, the system also promises to be generalizable for a wide range of walking speeds across the population. |
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The method utilizes force myography (FMG) signals from thighs while subjects walked on OG and ramp. Five adult male subjects wore a wireless FMG data acquisition system, developed in-house using force resistive sensors and electronic components. A heuristic approach for subject-dependent and terrain-independent model was developed to determine HS and TO in a given gait cycle in steady state and transition. The average error in HS determination was 9.66 ± 8.29, 9.38 ± 9.35, and 13.94 ± 18.95 ms, while TO was determined with an average error of 16.99 ± 18.12, 13.35 ± 15.10, and 17.29 ± 21.92 ms for OG, ramp, and transition, respectively. The proposed system is less expensive, simple to develop, and friendly to wear. The reported errors are comparable to previously reported errors using pressure sensitive insole, gyroscope, accelerometers, and electromyography, which are much complex and expensive in comparison to proposed FMG-based system. Although the tests were conducted on healthy subjects, the system promises to be generalizable to amputee and other pathological gaits also. While the tests were conducted on young adults at self-selected speeds, the system also promises to be generalizable for a wide range of walking speeds across the population.</description><identifier>ISSN: 0018-9456</identifier><identifier>EISSN: 1557-9662</identifier><identifier>DOI: 10.1109/TIM.2018.2816799</identifier><identifier>CODEN: IEIMAO</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Accelerometers ; Adults ; Electromyography ; Electronic components ; Event detection ; Force ; Force myography (FMG) ; Gait ; gait cycle ; heel strike (HS) ; Heuristic methods ; Legged locomotion ; locomotion ; Muscles ; Sensors ; toe-off (TO) ; transitions ; Walking</subject><ispartof>IEEE transactions on instrumentation and measurement, 2018-10, Vol.67 (10), p.2314-2323</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c338t-ccf5dea4450925c45555fa745277f9f90700f01fcdb524b11b3a10ef5ad3ca9b3</citedby><cites>FETCH-LOGICAL-c338t-ccf5dea4450925c45555fa745277f9f90700f01fcdb524b11b3a10ef5ad3ca9b3</cites><orcidid>0000-0001-7571-9130 ; 0000-0002-2438-3232 ; 0000-0002-6759-5980</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8335333$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27929,27930,54763</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8335333$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Godiyal, Anoop Kant</creatorcontrib><creatorcontrib>Verma, Hemant Kumar</creatorcontrib><creatorcontrib>Khanna, Nitin</creatorcontrib><creatorcontrib>Joshi, Deepak</creatorcontrib><title>A Force Myography-Based System for Gait Event Detection in Overground and Ramp Walking</title><title>IEEE transactions on instrumentation and measurement</title><addtitle>TIM</addtitle><description>In this paper, we present a novel method to determine the heel strike (HS) and toe-off (TO) during overground (OG) and ramp walking, including the transition. The method utilizes force myography (FMG) signals from thighs while subjects walked on OG and ramp. Five adult male subjects wore a wireless FMG data acquisition system, developed in-house using force resistive sensors and electronic components. A heuristic approach for subject-dependent and terrain-independent model was developed to determine HS and TO in a given gait cycle in steady state and transition. The average error in HS determination was 9.66 ± 8.29, 9.38 ± 9.35, and 13.94 ± 18.95 ms, while TO was determined with an average error of 16.99 ± 18.12, 13.35 ± 15.10, and 17.29 ± 21.92 ms for OG, ramp, and transition, respectively. The proposed system is less expensive, simple to develop, and friendly to wear. The reported errors are comparable to previously reported errors using pressure sensitive insole, gyroscope, accelerometers, and electromyography, which are much complex and expensive in comparison to proposed FMG-based system. Although the tests were conducted on healthy subjects, the system promises to be generalizable to amputee and other pathological gaits also. While the tests were conducted on young adults at self-selected speeds, the system also promises to be generalizable for a wide range of walking speeds across the population.</description><subject>Accelerometers</subject><subject>Adults</subject><subject>Electromyography</subject><subject>Electronic components</subject><subject>Event detection</subject><subject>Force</subject><subject>Force myography (FMG)</subject><subject>Gait</subject><subject>gait cycle</subject><subject>heel strike (HS)</subject><subject>Heuristic methods</subject><subject>Legged locomotion</subject><subject>locomotion</subject><subject>Muscles</subject><subject>Sensors</subject><subject>toe-off (TO)</subject><subject>transitions</subject><subject>Walking</subject><issn>0018-9456</issn><issn>1557-9662</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kN1LwzAUxYMoOKfvgi8Bnzvz0TTN45zbHGwMdOpjSNNkdm5NTbpB_3szNjxwuQ_3nHvgB8A9RgOMkXhazRYDgnA-IDnOuBAXoIcZ44nIMnIJeiieEpGy7BrchLBBCPEs5T3wOYQT57WBi86tvWq-u-RZBVPC9y60Zget83CqqhaOD6Zu4YtpjW4rV8OqhsuD8Wvv9nUJVZw3tWvgl9r-VPX6FlxZtQ3m7rz74GMyXo1ek_lyOhsN54mmNG8TrS0rjUpThgRhOmVRVvGUEc6tsAJxhCzCVpcFI2mBcUEVRsYyVVKtREH74PH0t_Hud29CKzdu7-tYKUnEgjHOEYkudHJp70LwxsrGVzvlO4mRPNKTkZ480pNnejHycIpUxph_e04po1F_Yc1qZA</recordid><startdate>20181001</startdate><enddate>20181001</enddate><creator>Godiyal, Anoop Kant</creator><creator>Verma, Hemant Kumar</creator><creator>Khanna, Nitin</creator><creator>Joshi, Deepak</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-7571-9130</orcidid><orcidid>https://orcid.org/0000-0002-2438-3232</orcidid><orcidid>https://orcid.org/0000-0002-6759-5980</orcidid></search><sort><creationdate>20181001</creationdate><title>A Force Myography-Based System for Gait Event Detection in Overground and Ramp Walking</title><author>Godiyal, Anoop Kant ; Verma, Hemant Kumar ; Khanna, Nitin ; Joshi, Deepak</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-ccf5dea4450925c45555fa745277f9f90700f01fcdb524b11b3a10ef5ad3ca9b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Accelerometers</topic><topic>Adults</topic><topic>Electromyography</topic><topic>Electronic components</topic><topic>Event detection</topic><topic>Force</topic><topic>Force myography (FMG)</topic><topic>Gait</topic><topic>gait cycle</topic><topic>heel strike (HS)</topic><topic>Heuristic methods</topic><topic>Legged locomotion</topic><topic>locomotion</topic><topic>Muscles</topic><topic>Sensors</topic><topic>toe-off (TO)</topic><topic>transitions</topic><topic>Walking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Godiyal, Anoop Kant</creatorcontrib><creatorcontrib>Verma, Hemant Kumar</creatorcontrib><creatorcontrib>Khanna, Nitin</creatorcontrib><creatorcontrib>Joshi, Deepak</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on instrumentation and measurement</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Godiyal, Anoop Kant</au><au>Verma, Hemant Kumar</au><au>Khanna, Nitin</au><au>Joshi, Deepak</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Force Myography-Based System for Gait Event Detection in Overground and Ramp Walking</atitle><jtitle>IEEE transactions on instrumentation and measurement</jtitle><stitle>TIM</stitle><date>2018-10-01</date><risdate>2018</risdate><volume>67</volume><issue>10</issue><spage>2314</spage><epage>2323</epage><pages>2314-2323</pages><issn>0018-9456</issn><eissn>1557-9662</eissn><coden>IEIMAO</coden><abstract>In this paper, we present a novel method to determine the heel strike (HS) and toe-off (TO) during overground (OG) and ramp walking, including the transition. The method utilizes force myography (FMG) signals from thighs while subjects walked on OG and ramp. Five adult male subjects wore a wireless FMG data acquisition system, developed in-house using force resistive sensors and electronic components. A heuristic approach for subject-dependent and terrain-independent model was developed to determine HS and TO in a given gait cycle in steady state and transition. The average error in HS determination was 9.66 ± 8.29, 9.38 ± 9.35, and 13.94 ± 18.95 ms, while TO was determined with an average error of 16.99 ± 18.12, 13.35 ± 15.10, and 17.29 ± 21.92 ms for OG, ramp, and transition, respectively. The proposed system is less expensive, simple to develop, and friendly to wear. The reported errors are comparable to previously reported errors using pressure sensitive insole, gyroscope, accelerometers, and electromyography, which are much complex and expensive in comparison to proposed FMG-based system. 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subjects | Accelerometers Adults Electromyography Electronic components Event detection Force Force myography (FMG) Gait gait cycle heel strike (HS) Heuristic methods Legged locomotion locomotion Muscles Sensors toe-off (TO) transitions Walking |
title | A Force Myography-Based System for Gait Event Detection in Overground and Ramp Walking |
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