Reliability and agreement of Azure Kinect and Kinect v2 depth sensors in the shoulder joint range of motion estimation

ÖZSOY U., YILDIRIM Y., Karaşin S., ŞEKERCİ R., Süzen L. B.

Journal of Shoulder and Elbow Surgery, vol.31, no.10, pp.2049-2056, 2022 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 10
  • Publication Date: 2022
  • Doi Number: 10.1016/j.jse.2022.04.007
  • Journal Name: Journal of Shoulder and Elbow Surgery
  • Journal Indexes: Science Citation Index Expanded, Scopus, EMBASE, MEDLINE
  • Page Numbers: pp.2049-2056
  • Keywords: 3D motion capture, Basic Science Study, depth sensors, digital analyses, Kinesiology, Marker-based motion analysis, markerless motion analysis, pose estimation


© 2022 Journal of Shoulder and Elbow Surgery Board of TrusteesBackground: 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. Methods: 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. Results: 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. Conclusion: 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.