Papers
5
Total Citations
130
H-Index
5
About
Emmanuel Guigon is a leading researcher in computational motor control, with a focus on understanding how the brain plans and executes movement and applying these principles to robotics and neurorehabilitation. His work bridges neuroscience, robotics, and clinical rehabilitation, exploring how biological motor control can inspire humanoid robot movements and how robotic training can modify pathological coordination patterns after stroke. Guigon's most cited paper (42 citations) demonstrates that training with a robotic exoskeleton can reshape upper-limb inter-joint coordination in healthy subjects, offering a foundation for stroke rehabilitation. Another highly influential study (38 citations) provides experimental evidence and a computational model suggesting that robotic and non-robotic arm training drive motor recovery through a common neural mechanism. He has also developed computational approaches to generate human-like reaching movements in humanoid robots, and investigated motor signatures in autism using human-machine interaction. Guigon's work is notable for its principled, neurobiologically-grounded approach to motor control, with applications ranging from assistive robotics to understanding developmental disorders. His research continues to shape how we design rehabilitation technologies and humanoid robots that move more naturally.
Research Focus
Key Achievements
Top Papers
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- 4Toward a motor signature in autism: Studies from human-machine interaction15 citations · 2018
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