Mohamed Jemni

Time Université, National Engineering School of Tunis

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About

Mohamed Jemni is a leading researcher in assistive technologies and human-computer interaction, with a focused expertise in sign language recognition and synthesis. His work bridges the gap between the Deaf community and digital systems through innovative deep learning and virtual character animation. Jemni’s major contributions include pioneering the use of electromyographic (EMG) signals for Arabic Sign Language alphabet recognition, achieving notable accuracy improvements that address the inherent challenges of gesture-based language processing. He also developed methods for animating signing avatars using descriptive sign language, enabling precise, interactive virtual characters that enhance communication accessibility. His research has garnered attention, with top-cited papers accumulating over 14 citations, reflecting its growing impact in assistive technology. Notably, Jemni’s work on EMG-driven handshape recognition offers promising alternatives for people with disabilities, extending beyond sign language to control drones, robots, and VR systems. By combining deep learning with physiological signals, he is advancing inclusive human-machine interfaces, making him a key figure in accessible technology innovation.