Correlation Engine 2.0
Clear Search sequence regions

Sizes of these terms reflect their relevance to your search.

Depth sensor-based motion analysis systems are of interest to researchers with low cost, fast analysis capabilities, and portability; thus, their reliability is a matter of interest. Our study examined the agreement and reliability in estimating the basic shoulder movements of Azure Kinect, Microsoft's state-of-the-art depth sensor, and its predecessor, Kinect v2, by comparing them with the gold standard marker-based motion analysis system. In our study, the shoulder joint ranges of motion of 20 healthy individuals were analyzed during dominant-side flexion, abduction, and rotation movements. The reliability and agreement between methods were evaluated using the intraclass correlation coefficient (ICC) and the Bland-Altman method. Compared to the gold standard method, the old- and new-generation Kinect showed similar performance in terms of reliability in the estimation of flexion (ICC = 0.86 vs. 0.82) and abduction (ICC = 0.78 vs. 0.79) movements, respectively. In contrast, the new-generation sensor showed higher reliability than its predecessor in internal (ICC = 0.49 vs. 0.75) and external rotation (ICC = 0.38 vs. 0.67) movement. Compared to its predecessor, Kinect Azure has higher reliability in analyzing movements in a lower range and variability, thanks to its state-of-the-art hardware. However, the sensor should also be tested on multiaxial movements, such as combing hair, drinking water, and reaching back, which are the tasks that simulate extremity movements in daily life. Copyright © 2022 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.


Umut Özsoy, Yılmaz Yıldırım, Sezen Karaşin, Rahime Şekerci, Lütfiye Bikem Süzen. Reliability and agreement of Azure Kinect and Kinect v2 depth sensors in the shoulder joint range of motion estimation. Journal of shoulder and elbow surgery. 2022 Oct;31(10):2049-2056

Expand section icon Mesh Tags

Expand section icon Substances

PMID: 35562032

View Full Text