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About

Seyed Amirhossein Mousavi is a researcher at the forefront of human-robot interaction and neural rehabilitation, specializing in the use of deep learning and electromyography (EMG) signals to restore and enhance human movement. His work bridges the gap between biological motor control and robotic exoskeletons, with a focus on developing intuitive interfaces for assistive technologies. Mousavi’s most cited paper, “Hand Movement Pattern Recognition Based on Convolutional Neural Network And AlexNet Architecture” (2020, 4 citations), pioneered the use of deep convolutional neural networks for classifying hand gestures, directly enabling more responsive control of wheelchairs, robots, and artificial prostheses. He further advanced this field with “Hand Movements Detection Using EMG Signals for Human-Computer Interface” (2024, 3 citations), a robust study involving 40 participants that validated a CNN-based approach for real-time hand movement detection. In parallel, Mousavi has made significant contributions to lower-limb rehabilitation, developing central pattern generator (CPG) pathways for exoskeleton control and designing human-robot interaction strategies for managing ankle and knee motion during walking. His work on “Production of CPG pathways for lower limbs” and “Management of human-robot interaction” (both 2020) directly addresses the needs of stroke survivors and individuals with traumatic brain injuries, offering new pathways to restore mobility and reduce daily living stress.