Journal article Open Access

A method for gait events detection based on low spatial resolution pressure insoles data

Salis, Francesca; Bertuletti, Stefano; Bonci, Tecla; Croce, Ugo Della; Mazzà, Claudia; Cereatti, Andrea


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.5526474</identifier>
  <creators>
    <creator>
      <creatorName>Salis, Francesca</creatorName>
      <givenName>Francesca</givenName>
      <familyName>Salis</familyName>
      <affiliation>Department of Biomedical Sciences, University of Sassari, Sassari, Italy; Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy;</affiliation>
    </creator>
    <creator>
      <creatorName>Bertuletti, Stefano</creatorName>
      <givenName>Stefano</givenName>
      <familyName>Bertuletti</familyName>
      <affiliation>Department of Biomedical Sciences, University of Sassari, Sassari, Italy; Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy;</affiliation>
    </creator>
    <creator>
      <creatorName>Bonci, Tecla</creatorName>
      <givenName>Tecla</givenName>
      <familyName>Bonci</familyName>
      <affiliation>Insigneo Institute for in silico Medicine and Department of Mechanical Engineering, University of Sheffield, Sheffield, UK</affiliation>
    </creator>
    <creator>
      <creatorName>Croce, Ugo Della</creatorName>
      <givenName>Ugo Della</givenName>
      <familyName>Croce</familyName>
      <affiliation>Department of Biomedical Sciences, University of Sassari, Sassari, Italy; Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Mazzà, Claudia</creatorName>
      <givenName>Claudia</givenName>
      <familyName>Mazzà</familyName>
      <affiliation>Insigneo Institute for in silico Medicine and Department of Mechanical Engineering, University of Sheffield, Sheffield, UK</affiliation>
    </creator>
    <creator>
      <creatorName>Cereatti, Andrea</creatorName>
      <givenName>Andrea</givenName>
      <familyName>Cereatti</familyName>
      <affiliation>Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A method for gait events detection based on low spatial resolution pressure insoles data</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>gait analysis</subject>
    <subject>wearable sensors</subject>
    <subject>pressure insoles</subject>
    <subject>locomotion</subject>
    <subject>gait events</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-08-13</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5526474</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsPublishedIn" resourceTypeGeneral="JournalArticle">10.1016/j.jbiomech.2021.110687</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5526473</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/mobilise-d</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Abstract: The accurate identification of initial and final foot contacts is a crucial prerequisite for obtaining a reliable estimation of spatio-temporal parameters of gait. Well-accepted gold standard techniques in this field are force platforms and instrumented walkways, which provide a direct measure of the foot&amp;ndash;ground reaction forces. Nonetheless, these tools are expensive, non-portable and restrict the analysis to laboratory settings. Instrumented insoles with a reduced number of pressure sensing elements might overcome these limitations, but a suitable method for gait events identification has not been adopted yet. The aim of this paper was to present and validate a method aiming at filling such void, as applied to a system including two insoles with 16 pressure sensing elements (element area&amp;nbsp;=&amp;nbsp;310&amp;nbsp;mm&lt;sup&gt;2&lt;/sup&gt;), sampling at 100&amp;nbsp;Hz. Gait events were identified exploiting the sensor redundancy and a cluster-based strategy. The method was tested in the laboratory against force platforms on nine healthy subjects for a total of 801 initial and final contacts. Initial and final contacts were detected with low average errors of (about 20&amp;nbsp;ms and 10&amp;nbsp;ms, respectively). Similarly, the errors in estimating stance duration and step duration averaged 20&amp;nbsp;ms and &amp;lt;10&amp;nbsp;ms, respectively. By selecting appropriate thresholds, the method may be easily applied to other pressure insoles featuring similar requirements.&lt;/p&gt;

&lt;p&gt;This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union&amp;#39;s Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this publication reflects the authors&amp;rsquo; view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/820820/">820820</awardNumber>
      <awardTitle>Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
148
87
views
downloads
All versions This version
Views 148148
Downloads 8787
Data volume 72.2 MB72.2 MB
Unique views 120120
Unique downloads 8282

Share

Cite as