Salih Ertug Ovur
Imperial College London, Politecnico di Milano, Istanbul Technical University
Papers
15
Total Citations
815
H-Index
10
About
Salih Ertug Ovur is a leading researcher at the intersection of robotics, artificial intelligence, and medical technology, with a primary focus on human-robot interaction for surgical applications. His work centers on developing intelligent, touch-free control systems for robot-assisted minimally invasive surgery, leveraging multi-sensor fusion—including depth vision, electromyographic signals, and recurrent neural networks—to enable natural hand gesture recognition and teleoperation. Ovur’s most-cited paper, “Multi-Sensor Guided Hand Gesture Recognition for a Teleoperated Robot Using a Recurrent Neural Network” (245 citations), demonstrates his pioneering approach to enhancing surgical robot autonomy and safety. His contributions extend to teaching by demonstration, where he has shown how robots can learn manipulation skills from open surgery (217 citations), and to adaptive shared control solutions that improve human-robot collaboration during operations. Ovur has also advanced the field with novel sensor fusion for hand pose estimation, IoT-based collaborative control, and a bioinspired virtual reality toolkit for medical education (BioVRbot). With over 800 total citations across his top ten papers, Ovur’s work is shaping the future of intelligent, human-aware robotic systems in healthcare.
Research Focus
Key Achievements
Top Papers
- 1
- 2Toward Teaching by Demonstration for Robot-Assisted Minimally Invasive Surgery217 citations · 2021
- 3
- 4Depth vision guided hand gesture recognition using electromyographic signals63 citations · 2020
- 5
- 6
- 7
- 8
- 9
- 10