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About

Mohd Azraai Mohd Razman is a researcher at the forefront of human-robot collaboration and intelligent automation, with key contributions spanning biomedical signal processing, rehabilitation robotics, and agricultural AI. His most impactful work, "The classification of movement intention through machine learning models: the identification of significant time-domain EMG features" (57 citations), pioneers the use of electromyography (EMG) signals to predict human motor intention—a critical enabler for seamless human-robot interaction. This research has significant implications for assistive technologies and collaborative industrial robots. In rehabilitation engineering, Razman developed a "Dynamic Ankle Foot Orthosis for lower limb rehabilitation" (16 citations), demonstrating expertise in system integration and control for gait disorder therapy. He has also advanced agricultural robotics through "Chili Plant Classification using Transfer Learning models through Object Detection" (5 citations), applying convolutional neural networks to enhance robotic vision for crop monitoring. His work on "Deep Learning Based Human Presence Detection" (2 citations) and "Fine-tuned RetinaNet models" (2 citations) addresses safety challenges in Industry 4.0 environments, enabling safer human-robot collaboration. Razman’s interdisciplinary research—bridging machine learning, biomechanics, and automation—positions him as a rising figure in intelligent systems, with his EMG-based intention prediction study serving as a cornerstone for future human-centered robotics.