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Validation of Azure Kinect for Upper Limb Motion Analysis Under Optimal and Suboptimal Conditions.

Gabriele Fassina¹, Serena Cerfoglio^2,3, Michele Rigucci¹

¹NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

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The Microsoft Azure Kinect (AK) system shows promise for upper-limb motion capture, accurately tracking movements within the camera

Area of Science:

Biomechanics and Movement Analysis
Clinical Kinematics
Markerless Motion Capture Technology

Background:

Upper-limb kinematics assessment is crucial for clinical applications like diagnosis and rehabilitation.
Markerless Motion Capture (MMC) systems offer a cost-effective alternative to traditional marker-based systems.
The accuracy of Microsoft Azure Kinect (AK) for upper-limb movements, especially in suboptimal conditions, requires further validation.

Purpose of the Study:

To validate the accuracy of the Microsoft Azure Kinect (AK) for upper-limb motion tracking.
To compare AK performance against a gold-standard optoelectronic system under optimal and suboptimal conditions.
To evaluate AK's reliability in assessing kinematic Performance Indicators (PIs).

Main Methods:

Keywords:

Azure Kinect kinematics markerless motion capture upper limb validity study

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An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

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Published on: March 28, 2018

Sixteen healthy adults performed ten upper body motor tasks.

Data collection occurred under optimal, table-occluded, and increased camera distance scenarios.

Joint angles were analyzed using normalized Root Mean Squared Error (nRMSE) and Pearson's correlation; PIs like Range of Motion (ROM), smoothness, and Time to Peak Velocity (TTPV) were evaluated.

Main Results:

AK demonstrated good accuracy (median nRMSE < 20%) for movements within the camera plane, even in suboptimal conditions.
Movements perpendicular to the camera plane were poorly captured.
ROM estimation was acceptable and reproducible; TTPV showed moderate-to-poor reliability, and smoothness deviated significantly from the reference.

Conclusions:

Microsoft Azure Kinect (AK) can accurately capture upper-limb movements within the camera plane, offering potential for clinical use.
Optimal Kinect positioning is critical for effective data acquisition, even in challenging environments.
Further research is needed to assess AK validity in clinical populations and multi-device setups.