Franco Bassetto
Papers
7
Total Citations
1,070
H-Index
6
About
Franco Bassetto is a prominent researcher specializing in rehabilitation robotics, prosthetics, and human-machine interfaces, with a particular focus on restoring hand functionality for amputees through non-invasive control technologies. His work sits at the intersection of biomedical engineering, machine learning, and signal processing, advancing the field of myoelectric prosthetic hand control. Bassetto's most influential contribution is his 2014 dataset paper on electromyography (EMG) data for naturally-controlled robotic hand prostheses, which has amassed an remarkable 931 citations, becoming a foundational resource for researchers worldwide developing non-invasive prosthetic control systems. This work, alongside his 2016 study examining how clinical parameters affect myoelectric control (66 citations), has significantly shaped understanding of the practical challenges facing real-world prosthetics deployment. A distinctive thread in Bassetto's research is his pioneering use of multimodal data fusion. Through projects like Megane Pro and subsequent datasets combining gaze tracking, visual, myoelectric, and inertial signals, he has pushed beyond EMG-only approaches to create richer, more robust control frameworks. His sustained output across dataset creation, clinical evaluation, and machine learning application demonstrates a comprehensive commitment to bridging laboratory innovation with meaningful rehabilitation outcomes for hand-amputated individuals.
Research Focus
Key Achievements
Top Papers
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