Laura Hallock and Robert Matthew and 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.

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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},
    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