Yumna Hajjar
Papers
1
Total Citations
9
H-Index
1
About
Yumna Hajjar is a rising researcher in biomedical engineering and human-computer interaction, whose work focuses on advancing neuroevolutionary algorithms for prosthetic control and assistive technologies. Her most-cited paper, "A novel neuroevolution model for EMG-based hand gesture classification" (2023), has garnered 9 citations and introduces an innovative approach that combines evolutionary computation with neural networks to decode electromyographic signals. This contribution is pivotal for developing more intuitive and adaptive prosthetic hands, as it enables real-time, accurate classification of hand gestures without extensive manual feature engineering. Hajjar’s research addresses key challenges in myoelectric control, such as robustness to signal variability and user-specific calibration. Her work stands out for its potential to improve the quality of life for amputees by creating smarter, more responsive prosthetics. As an early-career scholar, Hajjar’s growing citation impact signals her emerging influence in the field, and her novel neuroevolution model represents a significant step toward seamless human-machine interfaces.
Research Focus
Key Achievements
Top Papers
- 1A novel neuroevolution model for emg-based hand gesture classification9 citations · 2023