Daniele Riboni
Papers
3
Total Citations
34
H-Index
3
About
Daniele Riboni is pioneering the intersection of graph neural networks (GNNs) and high-density electromyography (HD EMG) to revolutionize movement intention recognition for amputees. His work addresses the critical challenge of decoding fine-grained gestures from dense electrode arrays, enabling more intuitive prosthetic control and human-computer interaction. In his 2022 study, Riboni demonstrated that GNNs can effectively model the spatial relationships among HD EMG channels, achieving robust recognition of grasping intentions—a breakthrough for assistive technologies. Building on this, his 2023 paper introduced explainable AI techniques to make GNN predictions transparent, enhancing trust and clinical adoption. With over 34 citations across these works, Riboni’s contributions are shaping next-generation prosthetics and gesture-based interfaces. Notably, he also explores novel applications like using air quality data and social robots to support food journaling, showcasing his versatility. His research not only advances machine learning for biomedical signals but also prioritizes interpretability, making him a key figure in human-centered AI.
Research Focus
Key Achievements
Top Papers
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