Project: Automated quantification of movement quality during standard clinical assessment using upper-extremity exoskeleton for stroke rehabilitation
When I look back on my life, I think I was raised and grew up as a natural “bridge” between different perspectives and disciplines. Born in an extended family, I often observed how my beloved family members could see and interpret the same event in the family from wildly different perspectives. As a first-born child, I often listened to their stories individually, realizing there might be some principles in people’s minds which led them to interpret varying events in life in a specific way. Maybe that’s why? From my childhood, I loved to observe nature and people, and I really wanted to know the fundamental principles that would explain the diversity in nature and people. I chose to study physics in my college, believing that physics would help me to do so. However, after college, I also realized that what I wanted to work on throughout my life was the biological system, especially the nervous system. I want to do so in both quantitative and qualitative ways. That was why I studied neuroscience for my Ph.D. study. I applied the neuroscientific background to neurorehabilitation because I found that the source of my pure joy was trying to make some positive changes in people’s lives, something that I learned from my family and volunteering experience at local churches.
What I do now as a scientist/engineer is to understand how the nervous system coordinates movement in health and disease and translate the knowledge into neurorehabilitation to improve motor function after neurological disorders. Working with stroke survivors, I have often used well-accepted clinical scales such as Fugl-Meyer Assessment in the upper extremity (FMA-UE) to describe their motor impairment for its convenience and reliability. Still, researchers know that the diversity of their motor impairment is beyond one clinical score. Understanding the diversity may lead the field to the next step of making personalized rehabilitation strategies. The following are what I questioned myself to develop my C-STAR-funded project: “Let’s assume that I have a stroke survivor and a measurement system that can capture what the person can perform within the system. Can I describe what impairments the person has in motor control in a fast, comprehensive, and accurate way? If so, how?”
For my C-STAR project, I will use a commercially available, three-dimensional, bimanual exoskeleton in the upper extremity. I will examine whether it is feasible to assess different aspects of a standard clinical assessment movement quality after stroke using the advanced robotic device. The robot could measure anatomical joint movements accurately enough to assess movements while wearing it. It is quick and easy for a stroke survivor to wear the robot. The post-processing time after data collection is expected to be short. The results of this study will provide a new automated way to quantify motor impairment by enhancing the standardization capability of the already established FMA-UE and by combining kinematic analysis and advanced robot used in the human arm. The developed system is expected to provide detailed and interpretable information on the sensory-motor performance of standardized motor tasks, which is desired for both research and clinical setting. Overall, the study outcome may be able to enhance arm stroke rehabilitation beyond conventional therapy alone by improving rehabilitation planning, recovery measurement, and evaluation of rehabilitation intervention. How the provided information will become “actionable” in a clinical setting is a current question I am trying to answer. I hope to receive feedback on this question from experts in Shirley Ryan AbilityLab.
When not doing academic or research-related activities, I enjoy walking with my husband, cooking Korean food, and meeting friends with different cultural backgrounds.
Assistant Professor, Department of Biomedical Engineering, University of Houston
Director, Rehabilitation Engineering for Improving Neuromotor Control (REIGN) Laboratory