Toshiaki Koike‐Akino
Papers
4
Total Citations
60
H-Index
3
About
Toshiaki Koike-Akino is a leading researcher at the intersection of control systems, human–machine interaction, and signal processing. His work focuses on enabling safer, more efficient networked systems, particularly in factory automation and remote robot manipulation. A key contribution is the co-design of safe and efficient networked control systems that account for state-dependent wireless fading channels, a foundational paper with 28 citations that addresses critical challenges in industrial automation. He has also advanced haptics and teleoperation, objectively comparing three feedback modalities for remote machine manipulation (24 citations), demonstrating how visual haptics can improve grasping force and task success rates. More recently, Koike-Akino has pioneered graph-based EEG signal compression for seamless human–machine interaction, and developed supervised learning approaches for online payload estimation in robot manipulators. His work bridges theoretical rigor with practical deployment, tackling real-world constraints like wireless uncertainty and computational efficiency. With a growing citation footprint and contributions spanning control, haptics, and bio-signal processing, Koike-Akino is shaping the future of intelligent, human-centered automation.
Research Focus
Key Achievements
Top Papers
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- 3Graph-Based EEG Signal Compression for Human–Machine Interaction6 citations · 2023
- 4