Bokkyu Kim, PT, PhD
RESEARCH PROGRAMS AND AFFILIATIONS
Brain Structural Plasticity
Motor Control and Motor Learning
ASSOCIATIONS / MEMBERSHIPS
1. Computer vision-based markerless motion capture for fine hand motor skill assessment. Computer vision, in combination with machine learning, enables us to record human movement kinematics more efficiently. We develop a motion capture system using action cameras to assess fine hand motor skills. This motion capture system can be used in future research for fine hand motor skill learning. Further, this motion capture system can be utilized in clinics to assess movement impairments.
2. Transcranial Photobiomodulation (tPBM) treatment - Illuminating the brain to enhance motor skill learning. Transcranial photobiomodulation (tPBM) treatment using near-infrared light-emitting diodes (NIR LED) is a relatively new non-invasive brain stimulation method that is proven safe and effective in inducing functional activation changes cerebral cortex in humans. We aim to develop a low-cost, easy-to-apply, safe, non-invasive brain stimulation treatment in conjunction with stroke rehabilitation interventions for sensorimotor deficits in chronic stroke survivors. Further, we aim to elucidate the neural mechanism(s) underlying transcranial photobiomodulation (tPBM) treatment for improvement in UE motor performance in chronic stroke survivors.
3. Fine hand motor skill learning and experience-dependent brain plasticity. Motor skill learning is a result of motor skill practice. Underlying neural mechanisms of motor skill learning have been studied for decades. Current neuroscientific evidence supports that experience-dependent neuroplasticity of the sensorimotor brain regions is the key neural mechanism of motor skill learning. My research team aims to determine how brain structure and function change after an intensive long-term upper extremity motor skill practice using MRI and neurophysiological assessments. Specifically, I am interested in the changes in brain structural connectivity of the sensorimotor brain regions in non-disabled adults and people with stroke using diffusion tensor imaging (DTI).
4. Effects of task conditions on upper extremity kinematics and compensatory movement strategies in chronic stroke survivors. Motor compensation is a learned motor behavior commonly observed in chronic stroke survivors. Due to the motor impairment, chronic stroke survivors develop movement strategies to substitute the motor impairment. Based on dynamic systems theory of motor control, individual, environmental, and task factors contribute to the choice of movement control strategies in chronic stroke survivors. We compare upper extremity kinematics and compensatory trunk movements during goal-directed arm reaches in different task conditions. Further, we also investigate how the environmental constraints can influence the arm reaching kinematics and trunk compensation.
5. Machine learning-based prediction modeling for motor recovery after stroke. This research project aims to develop a robust and accurate predictive model for post-stroke sensorimotor recovery using clinical outcome scores and neurological biomarkers derived from neuroimaging assessments. It is estimated that direct and indirect costs of stroke healthcare in 2030 will be about $ 70 billion dollars. Best way to reducing the stroke healthcare costs is to develop an accurate prognosis of sensorimotor recovery after stroke. Improving prognosis of sensorimotor recovery will help therapists to set realistic and achievable rehabilitation goals and to choose the most optimal therapeutic approach for each post-stroke individual. Thus, my research will contribute to reducing costs of stroke healthcare.