U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions | Zendy

Giuseppe Averta | Zendy; Federica Barontini | Zendy; Vincenzo Catrambone | Zendy; Sami Haddadin | Zendy; Giacomo Handjaras | Zendy; Jeremia P. O. Held | Zendy; Tingli Hu | Zendy; Eike Jakubowitz | Zendy; Christoph M. Kanzler | Zendy; Johannes Kühn | Zendy; Olivier Lambercy | Zendy; Andrea Leo | Zendy; Alina Obermeier | Zendy; Emiliano Ricciardi | Zendy; Anne Schwarz | Zendy; Gaetano Valenza | Zendy; Antonio Bicchi | Zendy; Matteo Bianchi | Zendy

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U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions

Author(s) -

Giuseppe Averta,

Federica Barontini,

Vincenzo Catrambone,

Sami Haddadin,

Giacomo Handjaras,

Jeremia P. O. Held,

Tingli Hu,

Eike Jakubowitz,

Christoph M. Kanzler,

Johannes Kühn,

Olivier Lambercy,

Andrea Leo,

Alina Obermeier,

Emiliano Ricciardi,

Anne Schwarz,

Gaetano Valenza,

Antonio Bicchi,

Matteo Bianchi

Publication year - 2021

Publication title -

gigascience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.947

H-Index - 54

ISSN - 2047-217X

DOI - 10.1093/gigascience/giab043

Subject(s) - physical medicine and rehabilitation , kinematics , computer science , rehabilitation , stroke (engine) , robotics , mechatronics , lower limb , upper limb , data collection , activities of daily living , motor control , rehabilitation robotics , artificial intelligence , human–computer interaction , medicine , robot , psychology , physical therapy , neuroscience , engineering , mechanical engineering , statistics , physics , surgery , mathematics , classical mechanics

Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, 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 data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization.

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