Center for Digital Health & Social Innovation, St. Pölten University of Applied Sciences, St. Pölten, Austria.
Institute of Health Sciences, Department of Health, St. Pölten University of Applied Sciences, St. Pölten, Austria.
Sci Data. 2024 Oct 8;11(1):1099. doi: 10.1038/s41597-024-03939-0.
This data descriptor introduces GaitRec-VR, a 3D gait analysis dataset consisting of 20 healthy participants (9 males, 11 females, age range 21-56) walking at self-selected speeds in a real-world laboratory and the virtual reality (VR) replicas of this laboratory. Utilizing a head-mounted display and a 12-camera motion capture system alongside a synchronized force plate, the dataset encapsulates real and virtual walking experiences. A direct kinematic model and an inverse dynamic approach were employed for kinematics and computation of joint moments respectively, with an average of 23 ± 6 steps for kinematics and five clean force plate strikes per participant for kinetic analysis. GaitRec-VR facilitates a deeper understanding of human movement in virtual environments, particularly focusing on dynamic balance during walking in healthy adults, crucial for effective VR applications in clinical settings. The dataset, available in both.c3d and.csv formats, allows further exploration into VR's impact on gait, bridging the gap between physical and virtual locomotion.
本数据描述符介绍了 GaitRec-VR，这是一个 3D 步态分析数据集，包含 20 名健康参与者（9 名男性，11 名女性，年龄在 21-56 岁之间）以自选速度在真实实验室和实验室虚拟现实（VR）复制品中行走。该数据集利用头戴式显示器和 12 个摄像机运动捕捉系统以及同步力板，封装了真实和虚拟行走体验。直接运动学模型和逆动力学方法分别用于运动学和关节力矩的计算，运动学平均有 23 ± 6 步，每位参与者有 5 次干净的力板冲击用于动力学分析。GaitRec-VR 有助于更深入地了解人类在虚拟环境中的运动，特别是关注健康成年人在 VR 环境中行走时的动态平衡，这对于临床环境中有效的 VR 应用至关重要。该数据集以.c3d 和.csv 格式提供，可进一步探索 VR 对步态的影响，弥合物理和虚拟运动之间的差距。
