Mitra Etemadi
Papers
1
Total Citations
4
H-Index
1
About
Mitra Etemadi is a researcher whose work sits at the intersection of deep learning and biomedical engineering, with a particular focus on human–machine interaction. Her most-cited paper, “Hand Movement Pattern Recognition Based on Convolutional Neural Network and AlexNet Architecture” (2020), introduces a novel method for classifying hand gestures using a convolutional neural network. This approach has direct applications in assistive technologies, including wheelchairs, robotic systems, and artificial hand prostheses. By leveraging the AlexNet architecture, Etemadi demonstrates how deep learning—traditionally dominant in image and speech processing—can be effectively adapted for real-time movement classification. Though her citation count is still growing, her work addresses a critical need for more intuitive and responsive control systems in rehabilitation and robotics. Etemadi’s research contributes to the broader effort of making assistive devices smarter and more accessible, positioning her as an emerging voice in the integration of neural networks with human motion analysis.
Research Focus
Key Achievements
Top Papers
- 1