Papers
14
Total Citations
1,703
H-Index
10
About
Arjan Gijsberts is a prominent researcher at the intersection of machine learning and rehabilitation robotics, with a particular focus on myoelectric control of robotic hand prostheses. His work has made significant strides in addressing one of the field's most persistent challenges: enabling amputees to intuitively and reliably control prosthetic hands through non-invasive techniques based on surface electromyography (sEMG). Among his most influential contributions is the 2014 release of a comprehensive electromyography dataset for prosthetic hand control, which has become a cornerstone reference in the field with over 930 citations, empowering researchers worldwide to develop and benchmark new control strategies. His work on stable myoelectric control using non-linear incremental learning and real-time Gaussian process regression (over 100 citations each) demonstrates his expertise in applying advanced machine learning to dynamic, real-world robotic systems. Gijsberts has also contributed to understanding how clinical parameters affect prosthetic hand control and has pioneered multimodal data acquisition integrating gaze, visual, and inertial signals alongside sEMG. His involvement in shaping community discourse through workshop proceedings further underscores his leadership role in advancing accessible, naturally controlled prosthetics for hand-amputated individuals.
Research Focus
Key Achievements
Top Papers
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- 7Incremental learning of robot dynamics using random features37 citations · 2011
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- 9Learning to Exploit Proximal Force Sensing: A Comparison Approach21 citations · 2009
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