A multimodal dataset of human gait at different walking speeds established on injury-free adult participants

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 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-1, 0.4–0.8 m.s-1, 0.8–1.2 m.s-1, 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 1.145 trials. This dataset could lead to increase the sample size of similar datasets, analyse the effect of walking speed on gait, or conduce unusual analysis of gait thanks to the full body markerset used. <br>

人体运动捕捉（Human motion capture）技术已被广泛应用于多个领域，用于分析、理解并复现日常生活活动中所需的各类运动多样性。
本研究提出的人体步态（gait）数据集，基于近6个月内上下肢无损伤、且过去两年未接受过上下肢手术的50名成年人构建而成。
受试者需在单次实验流程中于平直步道上以5种速度行走：0–0.4米每秒、0.4–0.8米每秒、0.8–1.2米每秒、自选自然步速以及快速步速。
同步采集了遍布全身的52个反光标记点（reflective markers）的三维运动轨迹、三维地面反作用力（ground reaction forces）及力矩，以及肌电信号（electromyographic signals）。
每位受试者在每种实验条件下至少完成3次有效试次，数据集总计包含1145次试次（原文标注为1.145，疑似排版笔误）。
本数据集可用于扩充同类数据集的样本规模，分析行走速度对步态的影响，同时依托所采用的全身标记点方案，可开展非常规步态相关研究。