Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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2008 - 200912020 - 20211

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    Estimating Biomechanical Risk Factors of Knee Osteoarthritis in Gait Using Instrumented Shoe Insole and Deep Learning Approaches
    (2021) Snyder, Samantha Jane; Miller, Ross; Shim, Jae Kun; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This study aims to implement an alternative to the cost-ineffective and time consuming current inverse dynamics approaches and predict knee adduction moments, a known predictor of knee osteoarthritis, through deep learning neural networks and a custom instrumented insole. Feed-forward, convolutional, and recurrent neural networks are applied to the data extracted from five piezo-resistive force sensors attached to the insole of a shoe. All models predicted knee adduction moment variables during walking with high correlation coefficients, greater than 0.72, and low root mean squared errors, ranging from 0.6-1.2%. The convolutional neural network is the most accurate predictor followed by the recurrent and feed-forward neural networks. These findings and the methods presented in the current study are expected to facilitate a cost-effective clinical analysis of knee adduction moments and to simplify future research studying the relationship between knee adduction moments and knee osteoarthritis.
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    Human Activity Classification Based on Gait and Support Vector Machines
    (2008) Ducao II, Amon Brigoli; Chellappa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Presented is a method to characterize human gait and to classify human activities using gait. Slices along the x-t dimension of a patio-temporal sequence are extracted to construct a gait double helical signature (gait DHS). A DHS pattern is a compact description that encodes the parameters of human gait and shows inherent symmetry in natural walking (without encumbered limb movement). The symmetry takes the form of Frieze groups, and differences in DHS symmetry can classify different activities. This thesis presents a method for extracting gait DHS, and how the DHS can be separable by activity. Then, a Support Vector Machine (SVM) n-class classifier is constructed using the Radial Basis Function (RBF) kernel, and the performance is measured on a set of data. The SVM is a classification tool based on learning from a training set, and fitting decision boundaries based on an output function. This thesis examines the effect of slicing at different heights of the body and shows the robustness of DHS to view angle, size, and direction of motion. Experiments using real video sequences are presented.