ArticlePloS one2022
Use of the Azure Kinect to measure foot clearance during obstacle crossing: A validation study.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Preliminary Concurrent Validation of a Portable Markerless Motion Capture System Against Three-Dimensional Gait Analysis Following Total Hip and Knee Arthroplasty.Sensors (Basel, Switzerland) · 2026Article
- Validation of Azure Kinect for Upper Limb Motion Analysis Under Optimal and Suboptimal Conditions.Sensors (Basel, Switzerland) · 2026Article
- Navigating multidimensional obstacles: a comparison between young and older adults and among obstacles.PloS one · 2026Article
- Frontiers in Three-Dimensional Surface Imaging Systems for 3D Face Acquisition in Craniofacial Research and Practice: An Updated Literature Review.Diagnostics (Basel, Switzerland) · 2024Review
- Accuracy and repeatability of the Microsoft Azure Kinect for clinical measurement of motor function.PloS one · 2023Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Obstacle crossing is typical adaptive locomotion known to be related to the risk of falls. Previous conventional studies have used elaborate and costly optical motion capture systems, which not only represent a considerable expense but also require participants to visit a laboratory. To overcome these shortcomings, we aimed to develop a practical and inexpensive solution for measuring obstacle-crossing behavior by using the Microsoft Azure Kinect, one of the most promising markerless motion capture systems. We validated the Azure Kinect as a tool to measure foot clearance and compared its performance to that of an optical motion capture system (Qualisys). We also determined the effect of the Kinect sensor placement on measurement performance. Sixteen healthy young men crossed obstacles of different heights (50, 150, and 250 mm). Kinect sensors were placed in front of and beside the obstacle as well as diagonally between those positions. As indices of measurement quality, we counted the number of measurement failures and calculated the systematic and random errors between the foot clearance measured by the Kinect and Qualisys. We also calculated the Pearson correlation coefficients between the Kinect and Qualisys measurements. The number of measurement failures and the systematic and random error were minimized when the Kinect was placed diagonally in front of the obstacle on the same side as the trail limb. The high correlation coefficient (r > 0.890) observed between the Kinect and Qualisys measurements suggest that the Azure Kinect has excellent potential for measuring foot clearance during obstacle-crossing tasks.
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