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About

Olfa Kanoun is a prominent researcher whose work sits at the intersection of sensor systems, human-robot interaction, and intelligent measurement technologies. Her research focuses on developing innovative sensing solutions and machine learning approaches that bridge the physical and digital worlds, with particular emphasis on gesture recognition, robotic systems, and industrial IoT applications. Among her notable contributions, Kanoun has pioneered edge computing approaches for real-time hand gesture classification using Electrical Impedance Tomography (EIT) measurements, addressing critical challenges in achieving accurate, low-latency human-robot interaction. Her work on gesture-based robot programming offers intuitive alternatives to traditional script-based systems, advancing agile manufacturing environments. She has also made strides in affordable humanoid robotics, developing the Fil3D-Hand — a life-size robotic hand incorporating nanocomposite strain sensors that replicate human dexterity at accessible cost. Kanoun's research extends to force myography-based gesture recognition and benchmarking kinetic transducers for industrial IMU applications, reflecting her broad expertise in sensor characterization. Her papers have collectively accumulated citations across multiple venues, demonstrating growing recognition in the robotics and measurement communities. Her work holds particular promise for assistive robotics, industrial automation, and accessible prosthetic technologies.