Semester

Summer

Date of Graduation

2020

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Sergiy Yakovenko

Committee Member

Victor Mucino

Committee Member

Osama Mukdadi

Abstract

Human movement is an instinctive yet challenging task that involves complex interactions between the neuromusculoskeletal system and its interaction with the surrounding environment. One key obstacle in the understanding of human locomotion is the availability and validity of experimental data or computational models. Corresponding measurements describing the relationships of the nervous and musculoskeletal systems and their dynamics are highly variable. Likewise, computational models and musculoskeletal models in particular are vitally dependent on these measurements to define model behavior and mechanics. These measurements are often sparse and disparate due to unsystematic data collection containing variable methodologies and reporting conventions. To date, there is not a framework to concatenate and manage musculoskeletal data (muscle moment arms and lengths). These morphological measurements need to be assembled to manage, compare, and analyze these data to develop comprehensive musculoskeletal models. Such a framework would enable researchers to select and update the posture-dependent relationships necessary to describe musculoskeletal dynamics, which are essential for simulation of muscle and joint torques in movement. Analogous to all simulations, these models require rigorous validation to ensure their accuracy. This is particularly important for musculoskeletal models that represent high-dimensional, posture-dependent relationships developed from limited and variable datasets. Here, I developed a computational workflow to collect and manage moment arm datasets from available published literature for the development of a human lower-limb musculoskeletal model. The moment arm relationships from multiple datasets were then used to create complete moment arm descriptions for all major leg muscles and were validated within a generic musculoskeletal model. These developments are crucial in advancing musculoskeletal modeling by providing standardized software and workflows for managing high-dimensional and posture-dependent morphological data to creating realistic and robust musculoskeletal models.

Embargo Reason

Publication Pending

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