George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA; Institute of Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA.
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
J Biomech. 2021 Apr 15;119:110320. doi: 10.1016/j.jbiomech.2021.110320. Epub 2021 Feb 20.
We introduce a novel dataset containing 3-dimensional biomechanical and wearable sensor data from 22 able-bodied adults for multiple locomotion modes (level-ground/treadmill walking, stair ascent/descent, and ramp ascent/descent) and multiple terrain conditions of each mode (walking speed, stair height, and ramp inclination). In this paper, we present the data collection methods, explain the structure of the open dataset, and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics as a function of the gait phase. This dataset offers a comprehensive source of locomotion information for the same set of subjects to motivate applications in locomotion recognition, developments in robotic assistive devices, and improvement of biomimetic controllers that better adapt to terrain conditions. With such a dataset, models for these applications can be either subject-dependent or subject-independent, allowing greater flexibility for researchers to advance the field.
我们引入了一个新的数据集，其中包含来自 22 名健康成年人的三维生物力学和可穿戴传感器数据，用于多种运动模式（平地/跑步机行走、楼梯上下、斜坡上下）和每种模式的多种地形条件（行走速度、楼梯高度和斜坡倾斜度）。在本文中，我们介绍了数据采集方法，解释了开放数据集的结构，并报告了传感器数据以及关节生物力学的运动学和动力学特征，这些特征是步态相位的函数。该数据集提供了同一组受试者的全面运动信息来源，以激发运动识别、机器人辅助设备的开发以及更好地适应地形条件的仿生控制器的改进等应用。有了这样的数据集，这些应用的模型可以是基于受试者的，也可以是独立于受试者的，这为研究人员提供了更大的灵活性来推进该领域的发展。
