Papers
5
Total Citations
44
H-Index
3
About
Laurent Peyrodie’s research lies at the intersection of rehabilitation robotics, human-robot interaction, and assistive technologies for individuals with motor disabilities. His major contributions include developing a slow walking model for children with multiple disabilities using humanoid robots—a pioneering approach that bridges robotics and pediatric rehabilitation. He has also advanced the field of exoskeleton control through mathematical modeling and PID control of lower extremity devices, addressing the growing needs of an aging population and those with physical impairments. More recently, Peyrodie has focused on predicting continuous limb joint angles from surface electromyography (sEMG) signals using deep learning architectures like Conv-BiLSTM and advanced signal processing techniques such as SA-FAWT, achieving high accuracy in human joint movement prediction. His work on wearable exoskeletons for upper limb dysfunction, particularly for stroke and spinal cord injury patients, underscores his commitment to improving human-robot collaboration. With over 44 citations across his most-cited works, Peyrodie’s research is gaining traction in the assistive robotics community. His ongoing exploration of mobile robots for search and rescue in underground mines further demonstrates his versatility in applying robotic solutions to critical real-world challenges.
Research Focus
Key Achievements
Top Papers
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