Doctor of Philosophy (PhD)
Electrical and Computer Engineering
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Neurorehabilitation, Controls Systems, Biomechanical signal processing
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Neurorehabilitation is a comprehensive approach aimed at helping patients regain motor control after a neural injury, including spinal cord injury, stroke, or other ischemic events. Early-stage neurorehabilitation is particularly delicate due to voluntary muscular weakness and lack of motor control, presenting in the form of spasticity. Unfortunately, this period of elevated weakness is when most neural control improvement can be made through a phenomenon called brain plasticity. Early rehabilitation traditionally requires a human therapist due to the adaptive and dynamic interpretation of undesired neuromuscular events. While efforts have been made to develop devices to aid in neurorehabilitation, the considerations that must be taken into account to design and develop an applicable, effective, and safe device can become a hindrance, preventing the proliferation of devices that could affect positive change in the communities that require them. Considerations for a neurorehabilitation device include sensor placement and usage, mechanical design, control system and design, physical interfacing, and user experience. In the following work, we first explore the physical design and development of an exoskeleton-type device, funded by the Department of Energy, that provides active assistive support to users and is therefore adaptable for early-stage neurorehabilitation patients. This device is capable of singular joint movement using a position-following controller with a manual interface. We employed serial elastic actuating modalities to stabilize displacement sensations and provided joint space feedback required for accurate displacement. We further include an analysis into control efficacy, wherein the average settling time for the position-based algorithm was of 2.02s, and the velocity algorithm performed at 3.04s. In terms of accuracy, the users were able to reach the desired positions within the 10 second time limit with 81% and 73% accuracy for the position control and velocity control, respectively. Following, we explore control mechanisms applicable to rehabilitative devices and define an admittance controller. We conclude parameterized control using biomechanical signals in an exoskeleton-type is viable, and including a feed-forward loop in the admittance controller provides the most coupled stability in the system following marginal analyses.
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Ramon, Rodrigo N., "System Design and Control Optimization for Neurorehabilitation Exoskeleton" (2022). FIU Electronic Theses and Dissertations. 4937.
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