Sahar Moghimi
Papers
9
Total Citations
92
H-Index
5
About
Sahar Moghimi is a leading researcher in bio-inspired robotics and rehabilitation engineering, specializing in the human masticatory system. Her work focuses on decoding the complex relationship between surface electromyography (sEMG) signals and jaw kinematics to develop intelligent rehabilitation aids. Moghimi’s major contributions include pioneering sEMG-based prediction of masticatory movements, achieving over 22 citations for her foundational work on rhythmic clenching, and advancing tele-operated robotic systems for jaw rehabilitation. She introduced a novel bio-inspired approach combining sEMG with Central Pattern Generators (CPG) to generate real-time jaw trajectories for chewing robots, a breakthrough with 16 citations. Her innovative use of hybrid neural networks and the Laguerre estimation technique to model biting force from EMG signals has deepened understanding of muscle-force dynamics. Moghimi also tackled complex kinematic challenges, such as solving the forward kinematics of Stewart-Gough platforms using improved hybrid strategies, accelerating robotic control. With over 90 total citations, her research bridges robotics, neuroscience, and clinical rehabilitation, offering transformative tools for patients with masticatory disorders. Her work stands out for its interdisciplinary rigor and practical impact on assistive technology design.
Research Focus
Key Achievements
Top Papers
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- 2Towards an SEMG-based tele-operated robot for masticatory rehabilitation22 citations · 2016
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