Heejun Kim
Papers
1
Total Citations
5
H-Index
1
About
Heejun Kim is a leading researcher in post-stroke rehabilitation, specializing in the intersection of robotic-assisted gait training (RAGT) and fall-risk assessment. His work focuses on identifying the optimal balance-related factors and RAGT attributes that can prevent falls in high-risk stroke survivors, a critical yet underexplored area in neurorehabilitation. In his landmark 2024 study, Kim applied clinical machine learning models to a cohort of 105 post-stroke patients, revealing key predictors of fall-related balance and effective training parameters. This research, which has already garnered 5 citations, bridges the gap between data-driven analytics and personalized rehabilitation protocols. By integrating artificial intelligence with clinical practice, Kim provides actionable insights for tailoring RAGT interventions, potentially reducing fall incidence and improving patient outcomes. His contributions are particularly notable for their translational impact, offering a framework for clinicians to optimize therapy based on individual patient profiles. Kim’s work is a vital resource for researchers and practitioners seeking to enhance the safety and efficacy of robotic rehabilitation in stroke recovery.
Research Focus
Key Achievements
Top Papers
- 1