CMEMS-UMinho, University of Minho, Guimarães, Portugal.
LABBELS-Associate Laboratory, Braga/Guimarães, Portugal.
Sci Data. 2022 Oct 6;9(1):603. doi: 10.1038/s41597-022-01722-7.
Monitoring gait and posture while using assisting robotic devices is relevant to attain effective assistance and assess the user's progression throughout time. This work presents a multi-camera, multimodal, and detailed dataset involving 14 healthy participants walking with a wheeled robotic walker equipped with a pair of affordable cameras. Depth data were acquired at 30 fps and synchronized with inertial data from Xsens MTw Awinda sensors and kinematic data from the segments of the Xsens biomechanical model, acquired at 60 Hz. Participants walked with the robotic walker at 3 different gait speeds, across 3 different walking scenarios/paths at 3 different locations. In total, this dataset provides approximately 92 minutes of total recording time, which corresponds to nearly 166.000 samples of synchronized data. This dataset may contribute to the scientific research by allowing the development and evaluation of: (i) vision-based pose estimation algorithms, exploring classic or deep learning approaches; (ii) human detection and tracking algorithms; (iii) movement forecasting; and (iv) biomechanical analysis of gait/posture when using a rehabilitation device.
在使用辅助机器人设备时监测步态和姿势对于实现有效的辅助和评估用户随时间的进展非常重要。本工作提出了一个多摄像机、多模态和详细的数据集，涉及 14 名健康参与者使用配备有一对经济实惠的摄像头的轮式机器人助行器行走。深度数据以 30 fps 采集，并与来自 Xsens MTw Awinda 传感器的惯性数据以及来自 Xsens 生物力学模型的节段的运动学数据同步，以 60 Hz 采集。参与者以 3 种不同的步行速度在 3 种不同的行走场景/路径在 3 个不同的位置使用机器人助行器行走。总的来说，这个数据集提供了大约 92 分钟的总记录时间，对应于将近 166000 个同步数据样本。该数据集可通过允许开发和评估以下内容为科学研究做出贡献：（i）基于视觉的姿势估计算法，探索经典或深度学习方法；（ii）人体检测和跟踪算法；（iii）运动预测；以及（iv）使用康复设备时的步态/姿势的生物力学分析。
