Papers
19
Total Citations
571
H-Index
10
About
Yasuharu Koike is a pioneering researcher at the intersection of neural engineering, human-machine interfaces, and bio-inspired robotics. His work centers on decoding biological signals—electromyography (EMG), electroencephalography (EEG), and electrooculography (EOG)—to create intuitive, real-time control systems for assistive robots and exoskeletons. Koike’s major contributions include developing a hybrid BMI-based exoskeleton for assisting paralyzed arm movements (94 citations) and a vision-guided robot arm controlled by a fusion of biosignals and head movement (75 citations), both advancing practical, non-invasive assistive technologies. His early work on a Kendama learning robot (175 citations) demonstrated bi-directional theory in motor skill acquisition, while his hierarchical network of nonlinear oscillators for hexapod locomotion (80 citations) showcases his impact on bio-inspired robotics. Koike has also explored user satisfaction in human-robot interaction, using EEG to reflect subjective experience. With over 500 total citations across these works, his research bridges fundamental neuroscience and applied robotics, offering students and researchers a compelling model for translating biological signals into fluid, adaptive machine control.
Research Focus
Key Achievements
Top Papers
- 1A Kendama Learning Robot Based on Bi-directional Theory175 citations · 1996
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- 5Endpoint stiffness magnitude increases linearly with a stronger power grasp22 citations · 2020
- 6Human interface using surface electromyography signals20 citations · 1996
- 7Robot Control Using Electromyography (EMG) Signals of the Wrist19 citations · 2005
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- 9Estimating joint angles from biological signals for multi-joint exoskeletons11 citations · 2014
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