Date of Award

2018

Degree Type

Dissertation

Degree Name

Doctor of Engineering

Department

Washkewicz College of Engineering

First Advisor

Bogert, Antonie van den

Subject Headings

Biomechanics, Mechanical Engineering, Rehabilitation

Abstract

Predictive simulations predict human gait by solving a trajectory optimization problem by minimizing energy expenditure. These simulations could predict the effect of a prosthesis on gait before its use. This dissertation has four aims, to show the application of predictive simulations in prosthesis design and to improve the quality of predictive simulations. Aim 1 was to explain joint moment asymmetry in the knee and hip in gait of persons with a transtibial amputation (TTA gait). Predictive simulations showed that an asymmetric gait required less effort. However, a small effort increase yielded a gait with increased joint moment symmetry and reduced joint reaction forces. This suggests that gait training could reduce the risk of developing osteoarthritis in persons with a transtibial amputation. Aim 2 was to compare the effect of different prosthesis alignments on TTA gait. Predictive simulations were solved using a three-dimensional musculoskeletal model with different prosthetic alignments. A flexion alignment of the prosthesis might be favored over a neutral alignment, since the metabolic cost and joint reaction forces were lower, though the differences were small. Also, predictions indicated that a lateral translation or an external rotation could alleviate skin problems by reducing skin-to-socket stresses. Aim 3 was to compare the gait objective of minimizing metabolic energy to minimizing muscular effort. Four metabolic energy expenditure models were selected after an experiment to compare metabolic cost calculated with seven metabolic energy models to metabolic cost from pulmonary gas exchange measurements. The minimum energy solution was more similar to normal gait in joint angles, while the minimum effort solution was more similar in joint moments, especially at the knee. However, neither solution could entirely explain human gait. Aim 4 was to propose an approach to optimize in a stochastic environment and implement it to explain antagonistic muscle co-contraction in human movement. In a stochastic environment, muscle co-contraction was energy optimal for certain tasks and nonzero foot clearance was energy efficient. The approach was then applied to TTA gait to explain co-contraction of the upper-leg muscles on the prosthesis side. The results suggested that antagonistic co-contraction is energy optimal for human gait.

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