Post-stroke Gait: Implications for Future Customizable Rehabilitation ApproachesCustomizable Rehabilitation Approaches

Unless this monitoring leads to EXACT PROTOCOLS to recover from the deficits in your walking, I can't see much use for this.  The mentors failed this research fellow by not specifying the proper outcome.

 Post-stroke Gait: Implications for Future Individuals with Post-stroke Gait: Implications for Future Customizable Rehabilitation Approaches Customizable Rehabilitation Approaches

To the Graduate Council:
I am submitting herewith a dissertation written by Azarang Asadi entitled “Motor
Control Quantification and Necessary Improvements for Individuals with Post-stroke
Gait: Implications for Future Customizable Rehabilitation Approaches.” I have
examined the final electronic copy of this dissertation for form and content and
recommend that it be accepted in partial fulfillment of the requirements for the degree
of Doctor of Philosophy, with a major in Biomedical Engineering.
Jeffrey A. Reinbolt, Major Professor
We have read this dissertation
and recommend its acceptance:
Zhenbo Wang
Emre Demirkaya
Michael A. Langston
Accepted for the Council:
Dixie L. Thompson
Vice Provost and Dean of the Graduate School
(Original signatures are on file with official student records.)Motor Control Quantification and
Necessary Improvements for Individuals with Post-stroke Gait:
Implications for Future Customizable Rehabilitation Approaches
A Dissertation Presented for the Doctor of Philosophy Degree
The University of Tennessee, Knoxville
Azarang Asadi
May 2024© by Azarang Asadi, 2024
All Rights Reserved.
To my mother, Mahbobeh, for all the support and sacrifices, and my sister, Mehregan,
for always believing in me.
Acknowledgements
I would like to thank and express my utmost gratitude to my advisor, Dr. Jeffrey
A. Reinbolt. His guidance and mentorship made this work possible and carried me
through all stages. His continuous support and patience made me feel competent,
and I am eternally grateful for being a graduate student in his research group.
I would like to thank the members of my dissertation committee, Dr. Michael
Langstong, Dr. Zhenbo Wang, and Dr. Emre Demirkaya for their time and valuable
comments. Their critical feedback and knowledgeable insights challenged me to
become a better scientist.
My appreciation extends to my fellow colleague at Reinbolt Research Group,
especially Ashley Rice who helped me tremendously with OpenSim and MATLAB.
I would like to extend my deepest gratitude to my mother. Mom, your selflessness
and sacrifices have been the bedrock of my academic journey. This accomplishment is
as much yours as it is mine, and I dedicate it to you with all my love and appreciation.
I am profoundly grateful to my sister whose enduring support, encouragement,
and belief in me have been a constant source of strength throughout my academic
journey. Her unwavering faith in me has been instrumental in achieving this milestone
and I am deeply thankful for her love and support.
I would like to thank my family and friends, especially Minou Attaei. Her support
and encouragement throughout this journey have been invaluable, and I am very
fortunate to have such a wonderful friend. Heartfelt thanks to my friend, Nobahar
Shahidi, for the support and guidance. I am grateful to have such a caring friend.
ivLast but not the least, I would like to give special thanks to my sweet dear feline
companions, Luna and Mah Banoo. Without their emotional support, none of this
work would have been possible. To my beloved cats, thank you for being my furry
writing companions throughout this journey.

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,
vistatistics, 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.