Master's Thesis

The thesis was successfully defended on Friday April 13th at 9:30 AM in Spencer Lab 209 at the University of Delaware. The thesis manuscript is also available here.

This thesis is a collection of two papers on EMG-driven modeling. In both papers, the modeling approach employs forward dynamics to estimate joint moments from muscle forces. During the optimization process, inverse dynamics is used as a benchmark to which the joint moments are compared to obtain values for necessary physiological parameters. Once the models are calibrated, they are used to predict muscle forces and joint moments on new trials and tasks. The papers deal with issues at the forefront of modeling in the field of biomechanics.

The first paper was published in the book Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques edited by Begg & Palaniswami. The focus of that book was to expose the reader to computational intelligence methods as applied to biomedical and human movement research areas. It was intended to provide an instructional approach to a very complex field. For this reason, chapter two is presented in a more detailed form than usually found in scientific journals – particularly outlining each of the steps necessary to implement a forward dynamic model. Two examples of the application of EMG-driven models are presented in the final sections of chapter two. The first is of a single-joint model applied to the ankle of an unimpaired subject during normal walking. In contrast, the second example is of the same model applied to a stroke subject during gait.

The second paper presented is the first report of a multi-joint EMG-driven model, and will be submitted to be published in a technical journal. The focus of the paper is the comparison between single-joint and multi-joint modeling. Of particular interest was the proper inclusion of biarticular muscles, which contribute to the joint moments produced at two joints. Accounting for the multiple actions of the gastrocnemii was hypothesized to deliver different muscle forces with similar joint moment estimations. The results supported the hypothesis, and generated some of the groundwork for proper implementation of multi-joint models.