Sensor-Driven Musculoskeletal Dynamic Modeling

Laura Hallock, Robert Matthew, Sarah Seko and Ruzena Bajcsy

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2016-66
May 12, 2016

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-66.pdf

The creation of a descriptive human dynamical model useful in upper-limb prosthesis and exoskeleton control remains an open problem. We here present a framework that approaches model generation from a ``sensor-driven'' design perspective that explicitly avoids over-fitting parameters and minimally relies on literature values and biological assumptions. We further apply this framework to a simplified dynamical model of the human elbow and verify using synthetic data that the problem of fitting this model to a real system is well-posed. Lastly, we apply the same simplified model to real surface electromyography (sEMG) and contact force data of a single subject. While the dynamical model extracted from this data is biologically nonsensical, the results indicate that this framework represents a viable starting point from which to build more sophisticated fully-recoverable dynamical models.


BibTeX citation:

@techreport{Hallock:EECS-2016-66,
    Author = {Hallock, Laura and Matthew, Robert and Seko, Sarah and Bajcsy, Ruzena},
    Title = {Sensor-Driven Musculoskeletal Dynamic Modeling},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2016},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-66.html},
    Number = {UCB/EECS-2016-66},
    Abstract = {The creation of a descriptive human dynamical model useful in upper-limb prosthesis and exoskeleton control remains an open problem. We here present a framework that approaches model generation from a ``sensor-driven'' design perspective that explicitly avoids over-fitting parameters and minimally relies on literature values and biological assumptions. We further apply this framework to a simplified dynamical model of the human elbow and verify using synthetic data that the problem of fitting this model to a real system is well-posed. Lastly, we apply the same simplified model to real surface electromyography (sEMG) and contact force data of a single subject. While the dynamical model extracted from this data is biologically nonsensical, the results indicate that this framework represents a viable starting point from which to build more sophisticated fully-recoverable dynamical models.}
}

EndNote citation:

%0 Report
%A Hallock, Laura
%A Matthew, Robert
%A Seko, Sarah
%A Bajcsy, Ruzena
%T Sensor-Driven Musculoskeletal Dynamic Modeling
%I EECS Department, University of California, Berkeley
%D 2016
%8 May 12
%@ UCB/EECS-2016-66
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-66.html
%F Hallock:EECS-2016-66