Papers
14
Total Citations
285
H-Index
8
About
Toshihiro Kawase is a pioneering researcher at the intersection of brain-machine interfaces (BMI), soft robotics, and assistive technologies. His work focuses on developing intuitive control systems for robotic exoskeletons and surgical tools, using neurophysiological signals like EEG and EMG. Kawase’s most cited paper (94 citations) introduces a hybrid BMI-based exoskeleton that enables real-time, asynchronous arm movement assistance for paralyzed individuals, blending EEG and EMG control for practical rehabilitation. He also advanced BMI-driven occupational therapy suits using SSVEP signals (62 citations), demonstrating how brain signals can restore functional movement. A hallmark of Kawase’s innovation is his use of pneumatic reservoir computing—exploiting air dynamics in soft robots for sensing and control, as seen in his work on soft exoskeletons (27 citations) and gait assistive suits (12 citations). This approach eliminates traditional electronic sensors, enhancing wearability and safety. Kawase has also explored cognitive neuroscience, showing how robotic arms alter body ownership and agency (25 citations), and applied deep learning to surgical suturing support (19 citations). With over 270 total citations, his contributions bridge neural engineering, soft robotics, and human-robot interaction, offering transformative solutions for rehabilitation and surgery.
Research Focus
Key Achievements
Top Papers
- 1
- 2A BMI-based occupational therapy assist suit: asynchronous control by SSVEP62 citations · 2013
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- 5Suturing Support by Human Cooperative Robot Control Using Deep Learning19 citations · 2020
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- 8Estimating joint angles from biological signals for multi-joint exoskeletons11 citations · 2014
- 9
- 10Development of Pneumatically Driven Surgical Robot for Catheter Ablation3 citations · 2020