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About

Youssef Amin is a researcher at the forefront of tactile sensing and embedded intelligence for robotics and prosthetics. His work centers on enabling robotic grippers and prosthetic limbs to perceive and classify the physical properties of objects in real time, using computationally efficient algorithms that run on resource-constrained devices. Amin’s major contributions include developing novel pre-processing and feature extraction techniques that allow single-layer feedforward neural networks to accurately classify object hardness and other tactile properties, achieving a critical trade-off between accuracy and computational cost. His most-cited paper, "Embedded real-time objects’ hardness classification for robotic grippers" (2023, 29 citations), demonstrates a practical system for real-time tactile perception. Beyond classification, Amin has explored artificial skin and electrotactile stimulation to restore somatosensory feedback in myoelectric prostheses, addressing a key limitation in modern prosthetic technology. His work has accumulated over 40 citations, reflecting growing interest in his computationally light, deployable solutions. Amin’s research is particularly impactful for students and engineers working on embedded systems, human-machine interfaces, and intelligent robotics, as it bridges the gap between advanced machine learning and real-world hardware constraints.