Author ORCID Identifier

https://orcid.org/0000-0002-3047-9708

Semester

Summer

Date of Graduation

2025

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Chemical and Biomedical Engineering

Committee Chair

Sergiy Yakovenko

Committee Member

Valeriya Gritsenko

Committee Member

Jessica Allen

Committee Member

Nicholas Szorcinski

Committee Member

Douglas Weber

Abstract

Despite how simple walking may seem, the locomotor control system is structurally and functionally complex. Its hierarchical organization of supraspinal and spinal networks with forward and feedback pathways has many interactions at multiple levels that are dependent on the dynamics of a high-dimensional musculoskeletal system. Having a comprehensive understanding of sensorimotor integration within a healthy locomotor control system is crucial for understanding changes to the system due to age or neurologic disease and developing effective technologies to recover mobility in those populations. In this dissertation, we address persistent gaps in knowledge pertaining to how the nervous system controls locomotion.

In Chapter 2, the basis of the dissertation is built upon the idea that the locomotor control system is organized such that the production of basic walking rhythms and patterns is managed by spinal mechanisms such as the central pattern generator and reflexes, while high-level control processes (e.g., navigation, precise stepping, adaptation, etc.) are managed by supraspinal mechanisms. This hierarchical organization allows for efficient control of locomotion, with potentially distinct modalities of information being controlled at each level. Animal and computational studies have suggested that low-level control signals encode information regarding muscle group behavior, however, less is known about the information encoded in the high-level control signals. Computational modelling of the central pattern generator has indicated that limb speed is encoded in high-level control signals, suggesting that high-level processes modulate whole-limb behavior. Here, we investigate this idea further through assessing high-level control signals via human kinesthesia, or the awareness of the position and movement one’s limbs. Specifically, we evaluate the sensitivity to limb speed perception, the integration of low-level to high-level sensory information in the afferent pathway, and the progression of limb speed perception across the human lifespan. With each of these aspects, we aim to provide experimental evidence in humans that signals encoded with information on limb speed drive the locomotor control system.

In Chapter 3, the scope of the dissertation is expanded to included not only how the locomotor control system operates within its normal set of movements, but also how it adapts and learns new movements. Classic studies have established that sensorimotor adaptation is demonstrated with key periods of adaptation and de-adaptation. The functional mechanism underlying this behavior involves recalibration of the internal model of limb dynamics; however, how to intentionally trigger this mechanism is still not clearly understood. Here, we test our hypothesis that a threshold in sensory error exists to regulate the initiation of sensorimotor adaptation.

In Chapter 4, the scope of the dissertation is expanded once again to include a potential application of locomotor control knowledge into assistive technologies for rehabilitation. Specifically, the assistive technology of interest is a system for gamified gait assessment in virtual reality that has intuitive control of navigation and obstacle avoidance. Recently developed self- paced treadmill algorithms are a promising solution for translating limb speed control into navigation within the virtual environment; however, a solution for translating limb kinematics for obstacle avoidance is still needed. Here, we propose the utilization of off-the-shelf virtual reality motion trackers as an accurate and virtual-reality-compatible method for motion capture. Towards this effort, we evaluate the accuracy of the tracker-based method for calculating joint angles within a physiological range of motion.

Overall, these studies will provide insight into various aspects of our understanding of locomotor control and will inform future rehabilitation protocols and assistive technologies.

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