Hiroshi Imamizu

Advanced Telecommunications Research Institute International, The University of Tokyo

Papers

Total Citations

105

H-Index

About

Hiroshi Imamizu is a leading figure in the fields of motor learning, computational neuroscience, and human-robot interaction. His research primarily investigates how the brain forms and updates internal models—neural representations that allow us to predict and control our movements. A key contribution is his work on the neural correlates of internal-model loading, which revealed how the brain adapts to novel dynamics during motor tasks, a foundational concept for understanding skill acquisition. Imamizu has also pioneered the study of how passive training, such as that delivered by upper extremity exoskeleton robots, affects proprioceptive acuity and motor learning. His work demonstrates that even without active movement, the brain can recalibrate its sensory predictions, offering powerful implications for rehabilitation and sports training. More recently, he has developed Bayesian estimation methods to predict the potential performance improvement elicited by robot-guided training, providing a statistical framework to optimize human-robot collaboration. With over 100 citations across his most influential papers, Imamizu’s work bridges neuroscience and robotics, offering both theoretical insights into brain function and practical tools for enhancing human motor performance.