Yuji Koike
Papers
2
Total Citations
8
H-Index
2
About
Yuji Koike is a researcher at the forefront of merging rehabilitation robotics with physical therapy education. His primary research areas include therapeutic motion analysis, human-robot interaction in rehabilitation, and the application of machine learning to assess clinical skills. Koike’s major contribution lies in developing quantitative methods to distinguish between novice and expert therapists using robotic devices. In his pivotal 2020 study, he employed a Support Vector Machine to analyze therapeutic motions on a robotic arm, achieving the ability to differentiate students from experienced therapists with high accuracy—a work that has garnered 6 citations and laid the groundwork for objective skill assessment. His 2021 feasibility study further advanced this line of inquiry by comparing manipulative indicators between students and therapists, identifying key motion elements essential for effective education. By translating subjective clinical expertise into measurable robotic parameters, Koike’s work offers a transformative pathway for standardizing exercise therapy training. His research not only enhances our understanding of therapeutic touch but also provides a scalable tool for improving the quality of rehabilitation education worldwide.
Research Focus
Key Achievements
Top Papers
- 1
- 2