Understanding the lower limb kinematic, kinetic, and electromyography (EMG) data interrelation in controlled speeds is challenging for fully assessing human locomotion conditions. This paper provides a complete dataset with the above-mentioned raw and processed data simultaneously recorded for sixteen healthy participants walking on a 10 meter-flat surface at seven controlled speeds (1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 km/h). The raw data include 3D joint trajectories of 24 retro-reflective markers, ground reaction forces (GRF), force plate moments, center of pressures, and EMG signals from Tibialis Anterior, Gastrocnemius Lateralis, Biceps Femoris, and Vastus Lateralis. The processed data present gait cycle-normalized data including filtered EMG signals and their envelope, 3D GRF, joint angles, and torques. This study details the experimental setup and presents a brief validation of the data quality. The presented dataset may contribute to (i) validate and enhance human biomechanical gait models, and (ii) serve as a reference trajectory for personalized control of robotic assistive devices, aiming an adequate assistance level adjusted to the gait speed and user’s anthropometry.

了解受控速度下的下肢运动学、动力学和肌电图 (EMG) 数据相互关系对于全面评估人体运动条件具有挑战性。本文提供了一个完整的数据集，其中同时记录了 16 名健康参与者以 7 种受控速度（1.0、1.5、2.0、2.5、3.0、3.5 和 4.0 公里/公里）在 10 米平坦的表面上行走的上述原始数据和处理数据。 h）。原始数据包括 24 个反光标记的 3D 关节轨迹、地面反作用力 (GRF)、测力板力矩、压力中心以及来自胫骨前肌、腓肠肌外侧肌、股二头肌和股外侧肌的EMG 信号。处理后的数据呈现步态周期标准化数据，包括滤波后的 EMG 信号及其包络、3D GRF、关节角度和扭矩。本研究详细介绍了实验设置，并对数据质量进行了简要验证。所提出的数据集可能有助于（ i ）验证和增强人类生物力学步态模型，以及（ ii ）作为机器人辅助设备个性化控制的参考轨迹，旨在根据步态速度和用户的人体测量调整适当的辅助水平。