Browsing by Subject "Gait"
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Item Algorithmically Recognizing Gait Variance from a Sensor-Based System(2019-05) Madden, JannaDetection of Vascular Dementia in early stages of Cognitive Impairment is difficult to do in a clinical setting since the earliest changes are often discrete and physiological in nature. One major aspect of this is gait patterns. This project utilizes force-sensing platforms, motion capture, and EMG sensors to unobtrusively collect biometric data from an individual’s walking gait patterns. Following data collection, a series of algorithms computes statistics off the gait cycles. In addition to previously validated biometric indicators of vascular dementia, including stride length, time in stride and swing phases of gait, time in dual leg vs single leg support, this system also examines metrics surrounding balance, lateral movement, and fine-grained gait analysis during critical transition periods of gait, when weight is transferred from one leg to the other. Secondly, by quantifying and analyzing machine learning algorithms, specifically deep learning time-series based models, onset patterns of vascular dementia are explored with an overarching goal of creating a system that will assist in understanding and diagnosing cases of vascular dementia. The proposed system provides a tool for which gait can be analyzed and compared over a long period of time and opens opportunity to increased personalization in health monitoring and disease diagnosis and provides an avenue to increase patient-centricity of medical care.Item Evaluating and Improving the Efficacy of Ankle Foot Orthoses for Children with Cerebral Palsy(2017-01) Ries, AndrewAnkle foot orthoses (AFOs) are commonly recommended for individuals with cerebral palsy (CP) as a means to improve gait. Goals of this dissertation were to evaluate the current efficacy of AFO use for children with CP, investigate the biomechanical mechanism of how AFOs influence gait, and describe new methods for analyzing and improving AFO outcomes as they pertain to gait. Retrospective data analysis, statistical machine learning, and simulation techniques were used to achieve these goals. Data analysis revealed that the general efficacy of AFO use was poor. However, a data driven model developed through machine learning techniques suggests that efficacy can likely be improved by using the model to recommend AFO prescriptions for individuals that are predicted to improve their gait with AFO use and refrain from prescribing AFOs for individuals whose gait will not improve with AFO use. Investigations of gait efficiency and muscle function revealed new factors that could potentially be leveraged to improve the efficacy of AFO use. Finally, an AFO design redundancy between two commonly prescribed AFOs was identified, eliminating misconceptions about the efficacy of a redundant AFO design. The techniques and conclusions presented in this dissertation have the potential to significantly improve the efficacy of AFO use for children with CP.