Date of Award

5-2018

Document Type

Thesis

Degree Name

Master of Science in Mechanical Engineering (MSME)

Department

Mechanical Engineering

First Advisor

Bradley Burchett

Second Advisor

David Purdy

Third Advisor

Rebecca Bercich

Abstract

Important in diagnosing gait abnormalities and pathologies is knowing the position of the leg at various points throughout the gait cycle. This is currently done with motion capture technology but the demand for Inertial Measurement Unit (IMU) based navigation and position tracking has been on the rise. A required component of this alternative is a gait model that can accurately predict the position of points of interest. In this thesis, a Kalman Filter is constructed using a contrived model to test if, given an accurate gait model, the filter can converge to an accurate and true position solution. Also presented is a Genetic Algorithm approach to dynamic system modeling. The dynamic system is made up of a four-bar linkage and has the ability to adapt to different gaits, both healthy and pathological. Results for the Kalman Filter are illustrated through convergence plots, and final position solutions and results for the Genetic Algorithm are given by position solutions of the four-bar linkage. These results show that a genetic approach is robust and has application in gait analysis

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