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Sensor-Driven Musculoskeletal Dynamic Modeling

Bajcsy, Ruzena
Hallock, Laura
Matthew, Robert
Seko, Sarah
Technical Report Identifier: EECS-2016-66
May 12, 2016

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.