Amina Ben Haj Amor

Papers

Total Citations

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About

Amina Ben Haj Amor is a leading researcher in assistive technology and human-computer interaction, with a specialized focus on sign language recognition using biosignals. Her work bridges the gap between machine learning and accessibility, particularly for Arabic Sign Language (ArSL) communities. Her most cited paper, "A deep learning based approach for Arabic Sign language alphabet recognition using electromyographic signals" (2021, 6 citations), pioneered the use of electromyographic (EMG) signals—electrical activity from muscles—to decode hand gestures, addressing the longstanding challenge of visual-based sign recognition. This approach offers a robust alternative to camera-dependent systems, making recognition possible in varied lighting or occluded environments. In her subsequent work, "Deep learning approach for sign language's handshapes recognition from EMG signals" (2022, 3 citations), she refined deep learning architectures to classify specific handshapes, demonstrating the potential of EMG signals for controlling assistive devices like drones, VR interfaces, and prosthetics. Her contributions are particularly impactful for people with disabilities, offering new pathways for communication and machine control. With a growing citation footprint, Ben Haj Amor is establishing herself as a key innovator at the intersection of deep learning, biosignal processing, and inclusive technology.