Domenico Buongiorno
Papers
11
Total Citations
744
H-Index
8
About
Domenico Buongiorno is a versatile researcher whose work spans computer vision, human-machine interaction, and medical robotics — fields where artificial intelligence meets real-world physical systems. His most celebrated contribution, a 2018 survey on deep learning and computer vision techniques for pedestrian detection and tracking, has amassed over 530 citations, establishing him as a key reference point in autonomous systems research. Equally significant is his sustained investigation into myoelectric control, where he has pioneered approaches that decode human motor intent from EMG signals using autoencoder-based neural architectures and muscle synergy extraction — work with direct implications for prosthetics, exoskeletons, and rehabilitation robotics. His research on neuromusculoskeletal modeling for shoulder and elbow joint control reflects a commitment to bridging neuroscience and engineering. Buongiorno has also contributed to robot-assisted surgery through deep learning-based image processing frameworks, and to industrial automation with AI-driven depalletization strategies. His work on bilateral teleoperation further demonstrates breadth, addressing stability challenges in haptic feedback systems. Collectively, his publications reflect an interdisciplinary vision: making intelligent machines more responsive, intuitive, and clinically meaningful for both industrial and human-centered applications.
Research Focus
Key Achievements
Top Papers
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- 9Real-Time 3D Tracker in Robot-Based Neurorehabilitation8 citations · 2018
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