Mi Young Oh

Papers

1

Total Citations

7

H-Index

1

About

Mi Young Oh’s research lies at the intersection of rehabilitation robotics and machine learning, with a focus on developing intelligent systems to support post-stroke motor recovery. Her most cited work introduces a novel approach to evaluating movement quality during unassisted pick-and-place exercises using a learning classifier, enabling autonomous robot therapy sessions to reinforce therapeutically desirable motions. This preliminary study, with 7 citations, demonstrates her commitment to creating adaptive, data-driven tools that can monitor patient performance in real time, reducing the need for constant therapist oversight. Oh’s contributions are particularly significant in the context of service robots for stroke rehabilitation, where she addresses the critical challenge of qualitative assessment in automated settings. Her work not only advances the technical integration of classifiers with robotic systems but also holds promise for expanding access to consistent, personalized therapy. By bridging engineering and clinical practice, Oh is helping to shape a future where robots can actively support and enhance human recovery, making rehabilitation more efficient and accessible for patients worldwide.

Research Focus

Key Achievements

1
H-Index
1
Papers
7
Total Citations
7
Avg Citations/Paper
🏆 Most Cited Paper
Learning classifier to evaluate movement quality in unassisted pick-and-place exercises for post-stroke patients: A preliminary study
7 citations · 2017
📈 Most Prolific Year: 2017 (1 Papers)
🤝 Key Collaborators: 4

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago