Table 1_Validation of Azure Kinect for upper limb kinematic assessment after arthroscopic rotator cuff repair: task-dependent accuracy and postoperative overestimation.docx

This study evaluated the concurrent validity of an Azure Kinect-based markerless system against a reference inertial measurement unit (IMU) system for upper limb kinematic assessment in patients undergoing RCT repair, and explored its postoperative clinical utility.</p>Methods<p>Twelve patients with unilateral full-thickness RCTs were assessed preoperatively and at four months postoperatively.

Participants performed six functional activities of daily living tasks and 3D shoulder range-of-motion (ROM) tests, with kinematic data synchronously collected by both systems. Inter-system agreement was evaluated using intraclass correlation coefficients (ICC2,5), Pearson’s r, Lin’s concordance correlation coefficient (CCC), Bland-Altman analysis, waveform similarity metrics (coefficient of multiple correlation - CMC), and error metrics (root mean square error-RMSE, mean absolute error-MAE, standard error of measurement-SEM).</p>Results<p>During functional tasks, Azure Kinect demonstrated good agreement with IMUs for sagittal-plane movements (ICC2,5 = 0.708 − 0.907 for shoulder flexion; 0.804 − 0.846 for elbow flexion) with good to excellent waveform consistency (CMC > 0.75; CCC > 0.70), but substantially lower agreement and waveform similarity in coronal and horizontal planes.