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Fall Prevention Using Linear and Nonlinear Analyses and Perturbation Training Intervention

Abstract Injuries and death associated with fall incidences pose a significant burden to society, both in terms of human suffering and economic losses. The main aim of this dissertation is to study approaches that can reduce the risk of falls. One major subset of falls is falls due to neurodegenerative disorders such as Parkinson’s disease (PD). Freezing of gait (FOG) is a major cause of falls in this population. Therefore, a new FOG detection method using wavelet transform technique employing optimal sampling window size, update time, and sensor placements for identification of FOG events is created and validated in this dissertation. Another approach to reduce the risk of falls in PD patients is to correctly diagnose PD motor subtypes. PD can be f... (more)
Created Date 2019
Contributor Rezvanian, Saba (Author) / Lockhart, Thurmon (Advisor) / Buneo, Christopher (Committee member) / Lieberman, Abraham (Committee member) / Abbas, James (Committee member) / Deep, Aman (Committee member) / Arizona State University (Publisher)
Subject Biomedical engineering / Biomechanics / Occupational therapy / Dynamic stability / Gait balance / Nonlinear analysis / Parkinson's disease / Perturbation training / Wavelet analysis
Type Doctoral Dissertation
Extent 141 pages
Language English
Note Doctoral Dissertation Biomedical Engineering 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis