UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

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 given thesis/dissertation in DRUM.

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

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Subject: search.filters.subject.Knee Adduction Moment

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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.