ArticleSensors (Basel, Switzerland)2023
How the Processing Mode Influences Azure Kinect Body Tracking Results.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed.
- Spatiotemporal attention-based dance motion recognition and intelligent correction system using 3D motion capture.Scientific reports · 2026Article
- Validation of Azure Kinect for upper limb kinematic assessment after arthroscopic rotator cuff repair: task-dependent accuracy and postoperative overestimation.Frontiers in bioengineering and biotechnology · 2026Article
- RGB-D Cameras and Brain-Computer Interfaces for Human Activity Recognition: An Overview.Sensors (Basel, Switzerland) · 2025Review
- Analysis of Kinect-Based Human Motion Capture Accuracy Using Skeletal Cosine Similarity Metrics.Sensors (Basel, Switzerland) · 2025Article
- Analysis of Factors Influencing the Precision of Body Tracking Outcomes in Industrial Gesture Control.Sensors (Basel, Switzerland) · 2024Article
- Evaluating Desk-Assisted Standing Techniques for Simulated Pregnant Conditions: An Experimental Study Using a Maternity-Simulation Jacket.Healthcare (Basel, Switzerland) · 2024Article
- Towards comparable quality-assured Azure Kinect body tracking results in a study setting-Influence of light.PloS one · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The Azure Kinect DK is an RGB-D-camera popular in research and studies with humans. For good scientific practice, it is relevant that Azure Kinect yields consistent and reproducible results. We noticed the yielded results were inconsistent. Therefore, we examined 100 body tracking runs per processing mode provided by the Azure Kinect Body Tracking SDK on two different computers using a prerecorded video. We compared those runs with respect to spatiotemporal progression (spatial distribution of joint positions per processing mode and run), derived parameters (bone length), and differences between the computers. We found a previously undocumented converging behavior of joint positions at the start of the body tracking. Euclidean distances of joint positions varied clinically relevantly with up to 87 mm between runs for CUDA and TensorRT; CPU and DirectML had no differences on the same computer. Additionally, we found noticeable differences between two computers. Therefore, we recommend choosing the processing mode carefully, reporting the processing mode, and performing all analyses on the same computer to ensure reproducible results when using Azure Kinect and its body tracking in research. Consequently, results from previous studies with Azure Kinect should be reevaluated, and until then, their findings should be interpreted with caution.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.