Doctoral Dissertations

Date of Award

5-2024

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Biomedical Engineering

Major Professor

Jeffrey A. Reinbolt

Committee Members

Zhenbo Wang, Emre Demirkaya, Michael A. Langston

Abstract

Although often taken for granted, walking is an extremely complex motor skill that requires sensory inputs, neural communication, advanced control strategies, and coordination of the muscles and joints. Electrical signals traveling from the brain to the muscles are transformed to mechanical forces to achieve desired motion. A stroke damages the central nervous system and neural pathways, limiting the ability of survivors to walk. Walking speed is significantly decreased and asymmetrical walking patterns emerge. A crucial component of stroke rehabilitation is gait training, a therapeutic intervention to help individuals to improve their walking ability, as walking is essential for functional independence and long-term survival.

Walking speed is often used as a gold standard for assessing the walking capabilities of stroke survivors, however, it’s important to note that a higher walking speed may not always indicate true recovery and may be a result of compensatory mechanisms. Monitoring the neurological impairment can improve our understanding of the walking disorder associated with stroke, guide the treatment according to patient’s specific needs, and contribute to development of new rehabilitation paradigms to improve the neuromuscular impairment.

In this work, we aim to establish a computational framework for real-time monitoring of walking ability of stroke survivors at a neural level, applicable for both gait laboratories and real-world settings. Additionally, we will investigate the capability of using such framework to improve the rehabilitation techniques for maximizing motor control complexity. We unite biomechanical modeling, simulations, statistics, and machine learning to achieve the goals of this research. First, we will investigate various quantitative measures of walking to understand their association with neurological impairment, and assess their potential for neuromuscular impairment monitoring purposes. Second, we will examine the utility of wearable sensors for assessing motor control complexity of stroke survivors during walking, with the aim of making assessments accessible beyond the gait laboratory. Lastly, we will investigate the muscle activity changes corresponding to motor control improvements of stroke survivors, in order to identify new rehabilitation paradigms to enhance the motor control complexity of post-stroke gait.

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