Doctoral Dissertations

Date of Award

12-2023

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Mechanical Engineering

Major Professor

Dustin L. Crouch

Committee Members

Jeffrey A. Reinbolt, Daniel Caleb Rucker, Songning Zhang

Abstract

Shoulder disability is a prevalent health issue associated with various orthopedic and neurological conditions, like rotator cuff tear and peripheral nerve injury. Many individuals with shoulder disability experience mild to moderate impairment and struggle with elevating the shoulder or holding the arm against gravity. To address this clinical need, I have focused my research on developing wearable passive exoskeletons that provide continuous at-home movement assistance. Through a combination of experiments and computational tools, I aim to optimize the design of these exoskeletons.

In pursuit of this goal, I have designed, fabricated, and preliminarily evaluated a wearable, passive, cam-driven shoulder exoskeleton prototype. Notably, the exoskeleton features a modular spring-cam-wheel module, allowing customizable assistive force to compensate for different proportions of the shoulder elevation moment due to gravity. The results of my research demonstrated that this exoskeleton, providing modest one-fourth gravity moment compensation at the shoulder, can effectively reduce muscle activity, including deltoid and rotator cuff muscles.

One crucial aspect of passive shoulder exoskeleton design is determining the optimal anti-gravity assistance level. I have addressed this challenge using computational tools and found that an assistance level within the range of 20-30% of the maximum gravity torque at the shoulder joint yields superior performance for specific shoulder functional tasks.

When facing a new task dynamic, such as wearing a passive shoulder exoskeleton, the human neuro-musculoskeletal system adapts and modulates limb impedance at the end-limb (i.e., hand) to enhance task stability. I have presented development and validation of a realistic neuromusculoskeletal model of the upper limb that can predict stiffness modulation and motor adaptation in response to newly introduced environments and force fields. Future studies will explore the model's applicability in predicting stiffness modulation for 3D movements in novel environments, such as passive assistive devices' force fields.

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