GaitRec-VR: 3D Gait Analysis for Walking Overground with and without a Head-Mounted-Display in Virtual Reality

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.

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Publication Details

Journal
Scientific Data
Published
2024-10-08
DOI
https://doi.org/10.1038/s41597-024-03939-0
Citations
3
Primary Topic
Balance, Gait, and Falls Prevention
Type
article
Field-Weighted Citation Impact
3.44

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article

GaitRec-VR: 3D Gait Analysis for Walking Overground with and without a Head-Mounted-Display in Virtual Reality

Bernhard Dumphart, Mark Simonlehner, Brian Horsak
3 citations
Scientific Data
Balance, Gait, and Falls Prevention
3.44
article

GaitRec-VR: 3D Gait Analysis for Walking Overground with and without a Head-Mounted-Display in Virtual Reality

Bernhard Dumphart, Mark Simonlehner, Brian Horsak
article en
3 citations

Abstract

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.

Scientific DataVol. 11(1)
University of Applied Sciences St Pölten (AT)
Österreichische Forschungsförderungsgesellschaft, Bundesministerium für Digitalisierung und Wirtschaftsstandort
Openalex Percentile: Top 6%
Balance, Gait, and Falls Prevention
3.44
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