Papers
45
Total Citations
812
H-Index
16
About
Tatsuo Narikiyo is a prominent robotics and control systems researcher whose work sits at the intersection of rehabilitation engineering, exoskeleton technology, and adaptive control theory. He has made substantial contributions to the development of robot-aided rehabilitation systems, particularly through pioneering assist-as-needed (AAN) control frameworks that maximize patient engagement by dynamically adjusting robotic assistance based on individual performance. His 2014 proof-of-concept study on upper limb rehabilitation using disturbance observers — now cited over 115 times — demonstrated how model-based compensation techniques could eliminate the need for cumbersome EMG and force sensors, significantly advancing practical exoskeleton design. Narikiyo has further extended this work into velocity field control, prescribed performance control, and neural network-based approaches for both upper and lower limb exoskeletons, with multiple papers accumulating between 25 and 100 citations. His research also spans quadrupedal locomotion and adaptive control for nonlinearly parameterized robotic systems. Collectively, his contributions have shaped how rehabilitation robots balance therapeutic effectiveness with patient autonomy, offering meaningful insights for clinicians, engineers, and researchers working to translate assistive robotics into real-world clinical settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2Field-Based Assist-as-Needed Control Schemes for Rehabilitation Robots100 citations · 2020
- 3
- 4An assist-as-needed control scheme for robot-assisted rehabilitation39 citations · 2017
- 5
- 6
- 7
- 8An Assist-as-Needed Velocity Field Control Scheme for Rehabilitation Robots26 citations · 2018
- 9
- 10