U-Limb

Shading light on the neuroscientific mechanisms of human upper limb motor control, both in healthy and pathological conditions (e.g. after a stroke event), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. At the same, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center dataset on human upper-limb movements, with the aim of fostering trans-disciplinary cross-fertilization. The dataset, which consists of data from 91 able bodied and 65 post-stroke participants, is organized at three levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electro-myography, electro-encephalography and electro-cardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; (iii) neural hand control using functional magnetic resonance imaging.