Iris Kyranou

Heriot-Watt University, University of Edinburgh

Papers

Total Citations

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About

Iris Kyranou is a leading researcher in the field of myoelectric control, with a primary focus on advancing prosthetic hand technologies and human-machine interaction. Her work centers on the real-time classification of multi-modal sensory data, where she has pioneered the integration of accelerometry with surface electromyography (sEMG) to significantly improve the performance and robustness of prosthetic devices. Her 2016 study on this topic, which has garnered 30 citations, demonstrates how combining these data streams can enhance classification accuracy, directly impacting the functionality of limb prosthetics. More recently, Kyranou has addressed critical challenges in myoelectric control systems, such as the variability caused by arm translation during gesture recognition. Her 2025 dataset, with 14 citations, provides a valuable resource for developing more accurate and robust control systems for applications ranging from prosthetics and teleoperation to immersive Metaverse interactions. Through her innovative contributions, Kyranou is shaping the future of intuitive, responsive prosthetic control and expanding the possibilities of human-machine interfaces.