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A machine learning approach to quantify individual gait responses to ankle exoskeletons
View ORCID ProfileMegan R. Ebers, View ORCID ProfileMichael C. Rosenberg, J. Nathan Kutz, View ORCID ProfileKatherine M. Steele
doi: https://doi.org/10.1101/2023.01.20.524757
Megan R. Ebers
aDepartment of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
Michael C. Rosenberg
aDepartment of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
bDepartment of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA
J. Nathan Kutz
cDepartment of Applied Mathematics, University of Washington, Seattle, WA, 98195, USA
Katherine M. Steele
aDepartment of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
Posted January 21, 2023.
A machine learning approach to quantify individual gait responses to ankle exoskeletons
Megan R. Ebers, Michael C. Rosenberg, J. Nathan Kutz, Katherine M. Steele
bioRxiv 2023.01.20.524757; doi: https://doi.org/10.1101/2023.01.20.524757
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