Elahe Rahimian
Papers
2
Total Citations
62
H-Index
2
About
Elahe Rahimian is a leading researcher in the intersection of artificial intelligence, deep learning, and neurorobotics, with a primary focus on advancing myoelectric control for prosthetic devices. Her major contributions center on developing novel deep-learning architectures for surface electromyography (sEMG)-based hand gesture recognition, a critical technology for improving the functionality and responsiveness of neuroprosthetic limbs. Her most cited work, "Semg-Based Hand Gesture Recognition Via Dilated Convolutional Neural Networks" (2019, 35 citations), introduced a pioneering dilated convolutional neural network that significantly enhanced gesture classification accuracy. Building on this, her 2020 paper (27 citations) proposed hybrid and dilated deep neural network architectures, specifically the [Formula: see text] and [Formula: see text] models, which further refined real-time myoelectric control. With a combined citation impact exceeding 60 from these foundational papers, Rahimian’s work is widely recognized for bridging advanced AI techniques with practical neurorobotic applications. Her research not only pushes the boundaries of assistive robotics but also holds transformative potential for amputees, offering more intuitive and precise control of prosthetic hands.
Research Focus
Key Achievements
Top Papers
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