Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait
The step-by-step determination of the spatio-temporal parameters of gait is clinically relevant since it provides an estimation of the variability of specific gait patterns associated with frequent geriatric syndromes. In recent years, several methods, based on the use of magneto-inertial units (MIM...
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description | The step-by-step determination of the spatio-temporal parameters of gait is clinically relevant since it provides an estimation of the variability of specific gait patterns associated with frequent geriatric syndromes. In recent years, several methods, based on the use of magneto-inertial units (MIMUs), have been developed for the step-by-step estimation of the gait temporal parameters. However, most of them were applied to the gait of healthy subjects and/or of a single pathologic population. Moreover, spatial parameters in pathologic populations have been rarely estimated step-by-step using MIMUs. The validity of clinically suitable MIMU-based methods for the estimation of spatio-temporal parameters is therefore still an open issue. The aim of this study was to propose and validate a method for the determination of both temporal and spatial parameters that could be applied to normal and heavily compromised gait patterns.
Two MIMUs were attached above each subject's ankles. An instrumented gait mat was used as gold standard. Gait data were acquired from ten hemiparetic subjects, ten choreic subjects, ten subjects with Parkinson's disease and ten healthy older adults walking at two different gait speeds. The method detects gait events (GEs) taking advantage of the cyclic nature of gait and exploiting some lower limb invariant kinematic characteristics. A combination of a MIMU axes realignment along the direction of progression and of an optimally filtered direct and reverse integration is used to determine the stride length.
Over the 4,514 gait cycles analyzed, neither missed nor extra GEs were generated. The errors in identifying both initial and final contact at comfortable speed ranged between 0 and 11 ms for the different groups analyzed. The stride length was estimated for all subjects with less than 3% error.
The proposed method is apparently extremely robust since gait speed did not substantially affect its performance and both missed and extra GEs were avoided. The spatio-temporal parameters estimates showed smaller errors than those reported in previous studies and a similar level of precision and accuracy for both healthy and pathologic gait patterns. The combination of robustness, precision and accuracy suggests that the proposed method is suitable for routine clinical use. |
doi_str_mv | 10.1186/1743-0003-11-152 |
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Two MIMUs were attached above each subject's ankles. An instrumented gait mat was used as gold standard. Gait data were acquired from ten hemiparetic subjects, ten choreic subjects, ten subjects with Parkinson's disease and ten healthy older adults walking at two different gait speeds. The method detects gait events (GEs) taking advantage of the cyclic nature of gait and exploiting some lower limb invariant kinematic characteristics. A combination of a MIMU axes realignment along the direction of progression and of an optimally filtered direct and reverse integration is used to determine the stride length.
Over the 4,514 gait cycles analyzed, neither missed nor extra GEs were generated. The errors in identifying both initial and final contact at comfortable speed ranged between 0 and 11 ms for the different groups analyzed. The stride length was estimated for all subjects with less than 3% error.
The proposed method is apparently extremely robust since gait speed did not substantially affect its performance and both missed and extra GEs were avoided. The spatio-temporal parameters estimates showed smaller errors than those reported in previous studies and a similar level of precision and accuracy for both healthy and pathologic gait patterns. The combination of robustness, precision and accuracy suggests that the proposed method is suitable for routine clinical use.</description><identifier>ISSN: 1743-0003</identifier><identifier>EISSN: 1743-0003</identifier><identifier>DOI: 10.1186/1743-0003-11-152</identifier><identifier>PMID: 25388296</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Accelerometry - instrumentation ; Accuracy ; Adults ; Aged ; Chorea - physiopathology ; Colleges & universities ; Error analysis ; Estimates ; Female ; Gait ; Gait Disorders, Neurologic - physiopathology ; Geriatrics ; Humans ; Invariants ; Kinematics ; Male ; Methods ; Middle Aged ; Mimus ; Neurosciences ; Paresis - physiopathology ; Parkinsonian Disorders - physiopathology ; Sensors ; Temporal logic ; Walking</subject><ispartof>Journal of neuroengineering and rehabilitation, 2014-11, Vol.11 (1), p.152-152</ispartof><rights>COPYRIGHT 2014 BioMed Central Ltd.</rights><rights>2014 Trojaniello et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.</rights><rights>Trojaniello et al.; licensee BioMed Central Ltd. 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b683t-b03653363be4ae7fe00fff7cf958a216e140cbfd23845b96be6f945c369ae5e23</citedby><cites>FETCH-LOGICAL-b683t-b03653363be4ae7fe00fff7cf958a216e140cbfd23845b96be6f945c369ae5e23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4242591/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4242591/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25388296$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Trojaniello, Diana</creatorcontrib><creatorcontrib>Cereatti, Andrea</creatorcontrib><creatorcontrib>Pelosin, Elisa</creatorcontrib><creatorcontrib>Avanzino, Laura</creatorcontrib><creatorcontrib>Mirelman, Anat</creatorcontrib><creatorcontrib>Hausdorff, Jeffrey M</creatorcontrib><creatorcontrib>Della Croce, Ugo</creatorcontrib><title>Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait</title><title>Journal of neuroengineering and rehabilitation</title><addtitle>J Neuroeng Rehabil</addtitle><description>The step-by-step determination of the spatio-temporal parameters of gait is clinically relevant since it provides an estimation of the variability of specific gait patterns associated with frequent geriatric syndromes. In recent years, several methods, based on the use of magneto-inertial units (MIMUs), have been developed for the step-by-step estimation of the gait temporal parameters. However, most of them were applied to the gait of healthy subjects and/or of a single pathologic population. Moreover, spatial parameters in pathologic populations have been rarely estimated step-by-step using MIMUs. The validity of clinically suitable MIMU-based methods for the estimation of spatio-temporal parameters is therefore still an open issue. The aim of this study was to propose and validate a method for the determination of both temporal and spatial parameters that could be applied to normal and heavily compromised gait patterns.
Two MIMUs were attached above each subject's ankles. An instrumented gait mat was used as gold standard. Gait data were acquired from ten hemiparetic subjects, ten choreic subjects, ten subjects with Parkinson's disease and ten healthy older adults walking at two different gait speeds. The method detects gait events (GEs) taking advantage of the cyclic nature of gait and exploiting some lower limb invariant kinematic characteristics. A combination of a MIMU axes realignment along the direction of progression and of an optimally filtered direct and reverse integration is used to determine the stride length.
Over the 4,514 gait cycles analyzed, neither missed nor extra GEs were generated. The errors in identifying both initial and final contact at comfortable speed ranged between 0 and 11 ms for the different groups analyzed. The stride length was estimated for all subjects with less than 3% error.
The proposed method is apparently extremely robust since gait speed did not substantially affect its performance and both missed and extra GEs were avoided. The spatio-temporal parameters estimates showed smaller errors than those reported in previous studies and a similar level of precision and accuracy for both healthy and pathologic gait patterns. The combination of robustness, precision and accuracy suggests that the proposed method is suitable for routine clinical use.</description><subject>Accelerometry - instrumentation</subject><subject>Accuracy</subject><subject>Adults</subject><subject>Aged</subject><subject>Chorea - physiopathology</subject><subject>Colleges & universities</subject><subject>Error analysis</subject><subject>Estimates</subject><subject>Female</subject><subject>Gait</subject><subject>Gait Disorders, Neurologic - physiopathology</subject><subject>Geriatrics</subject><subject>Humans</subject><subject>Invariants</subject><subject>Kinematics</subject><subject>Male</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Mimus</subject><subject>Neurosciences</subject><subject>Paresis - physiopathology</subject><subject>Parkinsonian Disorders - physiopathology</subject><subject>Sensors</subject><subject>Temporal logic</subject><subject>Walking</subject><issn>1743-0003</issn><issn>1743-0003</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNU01v1DAUjBCIlsKdE4rEhUNT_BE7CYdK1ap8SJW4wNlynOddt7EdbAdp_x6_DKdbli4qUuWDrfdm5tkzclG8xugM45a_x01NK4QQrTCuMCNPiuN96em981HxIsbrfKgRq58XR4TRtiUdPy5-XcZkrEzGu9LrMiaYqn5bLXsZp6VeJbCTD3IsJxmkhQQhLlDng81F6YbS2EmaAEO5liaVczRuXcaNdDeV9bNLuWHl2kHylXEQksm0CC76ED-UcppGo3YXSL6EcYAwbk_LDViTB0Iy6nSZfGMywRnpbieqjQ9g1O3Al8UzLccIr-72k-L7x8tvq8_V1ddPX1YXV1XPW5qqHlHOKOW0h1pCowEhrXWjdMdaSTAHXCPV64HQtmZ9x3vguquZoryTwIDQk-J8pzvNvYVBgUvZFTGF7F_YCi-NOOw4sxFr_1PUpCasw1lgtRPojf-PwGFHeSuWDMWSocBY5Iizyru7awT_Y4aYhDVRwThKB36OAvOaEMrrhj0CShmiqEOPUSVNxzAmNEPf_gO99nNw2foFxdu2Y4z_Ra3lCMI47fOT1CIqLhjtGkxQ22TU2QOovIYcv_IOtMn1AwLaEVTwMQbQe_swEsufeMiwN_dz2xP-fAL6GwwKCto</recordid><startdate>20141111</startdate><enddate>20141111</enddate><creator>Trojaniello, Diana</creator><creator>Cereatti, Andrea</creator><creator>Pelosin, Elisa</creator><creator>Avanzino, Laura</creator><creator>Mirelman, Anat</creator><creator>Hausdorff, Jeffrey M</creator><creator>Della Croce, Ugo</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7TB</scope><scope>7TK</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>L6V</scope><scope>LK8</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20141111</creationdate><title>Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait</title><author>Trojaniello, Diana ; Cereatti, Andrea ; Pelosin, Elisa ; Avanzino, Laura ; Mirelman, Anat ; Hausdorff, Jeffrey M ; Della Croce, Ugo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b683t-b03653363be4ae7fe00fff7cf958a216e140cbfd23845b96be6f945c369ae5e23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Accelerometry - instrumentation</topic><topic>Accuracy</topic><topic>Adults</topic><topic>Aged</topic><topic>Chorea - physiopathology</topic><topic>Colleges & universities</topic><topic>Error analysis</topic><topic>Estimates</topic><topic>Female</topic><topic>Gait</topic><topic>Gait Disorders, Neurologic - physiopathology</topic><topic>Geriatrics</topic><topic>Humans</topic><topic>Invariants</topic><topic>Kinematics</topic><topic>Male</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Mimus</topic><topic>Neurosciences</topic><topic>Paresis - physiopathology</topic><topic>Parkinsonian Disorders - physiopathology</topic><topic>Sensors</topic><topic>Temporal logic</topic><topic>Walking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Trojaniello, Diana</creatorcontrib><creatorcontrib>Cereatti, Andrea</creatorcontrib><creatorcontrib>Pelosin, Elisa</creatorcontrib><creatorcontrib>Avanzino, Laura</creatorcontrib><creatorcontrib>Mirelman, Anat</creatorcontrib><creatorcontrib>Hausdorff, Jeffrey M</creatorcontrib><creatorcontrib>Della Croce, Ugo</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Physical Education Index</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of neuroengineering and rehabilitation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Trojaniello, Diana</au><au>Cereatti, Andrea</au><au>Pelosin, Elisa</au><au>Avanzino, Laura</au><au>Mirelman, Anat</au><au>Hausdorff, Jeffrey M</au><au>Della Croce, Ugo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait</atitle><jtitle>Journal of neuroengineering and rehabilitation</jtitle><addtitle>J Neuroeng Rehabil</addtitle><date>2014-11-11</date><risdate>2014</risdate><volume>11</volume><issue>1</issue><spage>152</spage><epage>152</epage><pages>152-152</pages><issn>1743-0003</issn><eissn>1743-0003</eissn><abstract>The step-by-step determination of the spatio-temporal parameters of gait is clinically relevant since it provides an estimation of the variability of specific gait patterns associated with frequent geriatric syndromes. In recent years, several methods, based on the use of magneto-inertial units (MIMUs), have been developed for the step-by-step estimation of the gait temporal parameters. However, most of them were applied to the gait of healthy subjects and/or of a single pathologic population. Moreover, spatial parameters in pathologic populations have been rarely estimated step-by-step using MIMUs. The validity of clinically suitable MIMU-based methods for the estimation of spatio-temporal parameters is therefore still an open issue. The aim of this study was to propose and validate a method for the determination of both temporal and spatial parameters that could be applied to normal and heavily compromised gait patterns.
Two MIMUs were attached above each subject's ankles. An instrumented gait mat was used as gold standard. Gait data were acquired from ten hemiparetic subjects, ten choreic subjects, ten subjects with Parkinson's disease and ten healthy older adults walking at two different gait speeds. The method detects gait events (GEs) taking advantage of the cyclic nature of gait and exploiting some lower limb invariant kinematic characteristics. A combination of a MIMU axes realignment along the direction of progression and of an optimally filtered direct and reverse integration is used to determine the stride length.
Over the 4,514 gait cycles analyzed, neither missed nor extra GEs were generated. The errors in identifying both initial and final contact at comfortable speed ranged between 0 and 11 ms for the different groups analyzed. The stride length was estimated for all subjects with less than 3% error.
The proposed method is apparently extremely robust since gait speed did not substantially affect its performance and both missed and extra GEs were avoided. The spatio-temporal parameters estimates showed smaller errors than those reported in previous studies and a similar level of precision and accuracy for both healthy and pathologic gait patterns. The combination of robustness, precision and accuracy suggests that the proposed method is suitable for routine clinical use.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>25388296</pmid><doi>10.1186/1743-0003-11-152</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Accelerometry - instrumentation Accuracy Adults Aged Chorea - physiopathology Colleges & universities Error analysis Estimates Female Gait Gait Disorders, Neurologic - physiopathology Geriatrics Humans Invariants Kinematics Male Methods Middle Aged Mimus Neurosciences Paresis - physiopathology Parkinsonian Disorders - physiopathology Sensors Temporal logic Walking |
title | Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait |
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