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.

阐明健康与病理状态（例如脑卒中后）下人类上肢运动控制的神经科学机制，有助于开发用于定量评估受损状态的有效工具，并为康复流程提供科学指导。与此同时，机电一体化设备的设计与控制也可从这类神经科学研究成果中获益，对辅助与康复机器人学以及先进人机交互领域具有重要应用价值。为实现上述目标，我们认为全面收集人类行为数据是必不可少的环节。为此，我们发布了U-Limb，一款涵盖人类上肢运动的大型多模态多中心数据集，旨在推动跨学科交叉融合。该数据集共收录91名健康受试者与65名脑卒中后受试者的数据，分为三个层级进行组织：(i) 上肢日常生活活动数据，采集期间同步记录了运动学信号与生理信号（肌电图（electro-myography）、脑电图（electro-encephalography）与心电图（electro-cardiography））；(ii) 采用触觉设备完成精准操作任务时的力-运动学行为数据；(iii) 基于功能磁共振成像的手部神经控制数据。