Oussama El Ghoul
Papers
2
Total Citations
9
H-Index
2
About
Oussama El Ghoul is a researcher at the forefront of assistive technology, specializing in the intersection of deep learning and biomedical signal processing for sign language recognition. His work focuses on leveraging electromyographic (EMG) signals—electrical activity produced by muscles—as a novel, non-visual input for gesture interpretation. El Ghoul’s major contributions include pioneering deep learning architectures that decode handshapes and alphabet gestures from EMG data, offering a promising alternative to traditional camera-based systems that struggle with lighting and occlusion. His 2021 paper, "A deep learning based approach for Arabic Sign language alphabet recognition using electromyographic signals," has garnered 6 citations, while his 2022 follow-up on handshape recognition has received 3 citations. These works address a critical gap in assistive technologies for deaf and hard-of-hearing communities, particularly for Arabic Sign Language, which has received limited computational attention. By demonstrating that muscle signals can reliably encode linguistic gestures, El Ghoul is helping to create more robust, privacy-preserving, and accessible communication tools. His research represents a significant step toward practical, wearable sign language translation systems.
Research Focus
Key Achievements
Top Papers
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- 2