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oai:figshare.com:article/33962119 2026-09-22T05:38:33Z category_21 portal_316 item_type_3 month_year_09_2026

Table 1_Validation of Azure Kinect for upper limb kinematic assessment after arthroscopic rotator cuff repair: task-dependent accuracy and postoperative overestimation.docx Yu-fei Chen (12373032) Chuang-yu Zhan (25087885) Ling-jie Zhang (14504211) Yue-xin Zhang (25087888) Yun-ru Ma (25087891) Biotechnology arthroscopic repair Azure Kinect depth sensor inertial measurement unit markerless motion analysis rotator cuff tear upper limb movement analysis Introduction<p>Arthroscopic rotator cuff repair is the standard treatment for rotator cuff tears (RCTs), yet postoperative functional evaluation continues to rely heavily on subjective clinical scales, with limited objective kinematic assessment. 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. Bland–Altman analysis showed relatively small sagittal-plane errors (SEM < 15°) but larger biases in non-sagittal planes, with proportional bias detected for most joint planes and tasks (p < 0.05). For 3D shoulder ROM assessment, good agreement was observed for flexion (ICC2,5 = 0.837), abduction (ICC2,5 = 0.729), and rotation (ICC2,5 = 0.735). No significant preoperative inter-system differences were found. Postoperatively, Azure Kinect significantly overestimated flexion (137.9 ± 13.5° vs. 97.1 ± 25.1°, p = 0.002) and external rotation (48.6 ± 14.6° vs. 31.0 ± 14.4°, p = 0.038). Both systems detected significant flexion changes pre-to-post, but neither detected changes in abduction or rotation.</p>Discussion<p>These preliminary findings indicate task- and plane-dependent validity for the Azure Kinect, with good sagittal performance but substantial errors in multi-planar motions and postoperative overestimation. The system may serve as a screening tool for tracking postoperative functional recovery, but its current accuracy is insufficient for standalone surgical outcome assessment. Results should be interpreted with caution given the small sample size and confirmed in larger cohorts.</p> 2026-09-22T05:38:33Z Dataset Dataset 10.3389/fbioe.2026.1927386.s001 https://figshare.com/articles/dataset/Table_1_Validation_of_Azure_Kinect_for_upper_limb_kinematic_assessment_after_arthroscopic_rotator_cuff_repair_task-dependent_accuracy_and_postoperative_overestimation_docx/33962119 CC BY 4.0