Centre National de Rééducation Fonctionnelle et de Réadaptation - Rehazenter, Laboratoire d'Analyse du Mouvement et de la Posture, Luxembourg, Luxembourg.
Sci Data. 2019 Jul 3;6(1):111. doi: 10.1038/s41597-019-0124-4.
Human motion capture is used in various fields to analyse, understand and reproduce the diversity of movements that are required during daily-life activities. The proposed dataset of human gait has been established on 50 adults healthy and injury-free for lower and upper extremities in the most recent six months, with no lower and upper extremity surgery in the last two years. Participants were asked to walk on a straight level walkway at 5 speeds during one unique session: 0-0.4 m.s, 0.4-0.8 m.s, 0.8-1.2 m.s, self-selected spontaneous and fast speeds. Three dimensional trajectories of 52 reflective markers spread over the whole body, 3D ground reaction forces and moment, and electromyographic signals were simultaneously recorded. For each participants, a minimum of 3 trials per condition have been made available in the dataset for a total of 1143 trials. This dataset could increase the sample size of similar datasets, lead to analyse the effect of walking speed on gait or conduct unusual analysis of gait thanks to the full body markerset used.
人体运动捕捉在各个领域中被用于分析、理解和再现日常生活活动中所需的各种运动。本研究提出的下肢和上肢步态数据集是基于最近六个月内 50 名健康且无损伤的成年人建立的，且在过去两年内没有进行过下肢和上肢手术。要求参与者在一个单一的测试 session 中以 5 种不同速度在水平直道上行走：0-0.4 m.s、0.4-0.8 m.s、0.8-1.2 m.s、自主选择的自然速度和快速速度。同时记录了全身 52 个反射标记的三维轨迹、三维地面反作用力和力矩以及肌电图信号。对于每个参与者，在数据集内每个条件下至少有 3 次试验，总共有 1143 次试验。这个数据集可以增加类似数据集的样本量，可以通过使用全身标记来分析行走速度对步态的影响，或者进行不同寻常的步态分析。
