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dc.contributor.authorÖzsoy, Ümit
dc.contributor.authorYıldırım, Yılmaz
dc.contributor.authorKaraşin, Sezen
dc.contributor.authorŞekerci, Rahime
dc.contributor.authorSüzen, Lütfiye Bikem
dc.date.accessioned2023-05-03T11:37:28Z
dc.date.available2023-05-03T11:37:28Z
dc.date.issued2022
dc.identifier.citationÖzsoy, Ü., Yıldırım, Y., Karaşin, S., Şekerci, R. & Süzen, L. B. (2022). 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, 31(10), 2049-2056.en_US
dc.identifier.issn1058-2746
dc.identifier.urihttp://hdl.handle.net/20.500.12566/1565
dc.description.abstractBackground: 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 co- efficient (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 ¼ 0.86 vs. 0.82) and abduction (ICC ¼ 0.78 vs. 0.79) movements, respectively. In contrast, the new- generation sensor showed higher reliability than its predecessor in internal (ICC ¼ 0.49 vs. 0.75) and external rotation (ICC ¼ 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, drink- ing water, and reaching back, which are the tasks that simulate extremity movements in daily life.en_US
dc.description.sponsorshipNo sponsoren_US
dc.language.isoengen_US
dc.publisherJournal of Shoulder and Elbow Surgeryen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMarker-based motion analysisen_US
dc.subjectİşaretçi tabanlı hareket analizitr_TR
dc.subjectMarkerless motion analysisen_US
dc.subjectİşaretsiz hareket analizitr_TR
dc.subject3D motion captureen_US
dc.subject3D hareket yakalamatr_TR
dc.subjectDepth sensorsen_US
dc.subjectDerinlik sensörlerien_US
dc.subjectPose estimationen_US
dc.subjectPoz tahminitr_TR
dc.subjectDigital analysisen_US
dc.subjectDijital analiztr_TR
dc.titleReliability and agreement of Azure Kinect and Kinect v2 depth sensors in the shoulder joint range of motion estimationen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.relation.publicationcategoryInternational publicationen_US
dc.identifier.wosWOS:000860337700014
dc.identifier.scopus2-s2.0-85133137224
dc.identifier.volume31
dc.identifier.issue10
dc.identifier.startpage2049
dc.identifier.endpage2056
dc.contributor.orcid0000-0003-2293-9388 [Süzen, Lütfiye Bikem]
dc.contributor.abuauthorSüzen, Lütfiye Bikem
dc.contributor.yokid3475 [Süzen, Lütfiye Bikem]
dc.contributor.ScopusAuthorID56320355200 [Süzen, Lütfiye Bikem]
dc.identifier.PubMedID35562032
dc.identifier.doi10.1016/j.jse.2022.04.007


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