Tomoya Tamei
Papers
15
Total Citations
561
H-Index
12
About
Tomoya Tamei is a leading researcher at the intersection of neural interfaces, rehabilitation robotics, and assistive manipulation. His work centers on two transformative areas: decoding human motor intent from surface electromyography (EMG) signals and enabling robots to perform complex physical assistance tasks through learning. Tamei’s most influential contribution is the development of continuous and simultaneous estimation of finger kinematics from EMG-to-muscle activation models (199 citations), a breakthrough that has advanced proportional control for prosthetic hands and rehabilitation exoskeletons. He has further refined this approach using multi-output Gaussian processes to estimate multi-degree-of-freedom finger joint angles, enabling more natural human-robot interaction. In parallel, Tamei pioneered robotic clothing assistance—a challenging problem involving non-rigid materials and real-time state estimation. His work on Bayesian nonparametric learning of cloth models (40 citations) and reinforcement learning for dual-arm dressing assistance (100 citations) has established a foundation for assistive robots that can adapt to deformable objects. Tamei’s research, consistently published in top robotics and biomedical engineering venues, has accumulated over 500 citations, demonstrating its impact on both neural decoding and assistive robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Reinforcement learning of clothing assistance with a dual-arm robot100 citations · 2011
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10