{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T06:05:27Z","timestamp":1790057127399,"version":"4.0.1"},"reference-count":58,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioeng. Biotechnol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats: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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats: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\u2019s r, Lin\u2019s 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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>During functional tasks, Azure Kinect demonstrated good agreement with IMUs for sagittal-plane movements (ICC2,5 = 0.708 \u2212 0.907 for shoulder flexion; 0.804 \u2212 0.846 for elbow flexion) with good to excellent waveform consistency (CMC &amp;gt; 0.75; CCC &amp;gt; 0.70), but substantially lower agreement and waveform similarity in coronal and horizontal planes. Bland\u2013Altman analysis showed relatively small sagittal-plane errors (SEM &amp;lt; 15\u00b0) but larger biases in non-sagittal planes, with proportional bias detected for most joint planes and tasks (p &amp;lt; 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 \u00b1 13.5\u00b0 vs. 97.1 \u00b1 25.1\u00b0, p = 0.002) and external rotation (48.6 \u00b1 14.6\u00b0 vs. 31.0 \u00b1 14.4\u00b0, p = 0.038). Both systems detected significant flexion changes pre-to-post, but neither detected changes in abduction or rotation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats: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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbioe.2026.1927386","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T05:40:32Z","timestamp":1790055632000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Validation of Azure Kinect for upper limb kinematic assessment after arthroscopic rotator cuff repair: task-dependent accuracy and postoperative overestimation"],"prefix":"10.3389","volume":"14","author":[{"given":"Yu-fei","family":"Chen","sequence":"first","affiliation":[{"name":"Department of the First School of Clinical Medicine, The First Affiliated Hospital of Fujian Medical University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuang-yu","family":"Zhan","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, School of Health, Fujian Medical University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling-jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, School of Health, Fujian Medical University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue-xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University","place":["Fuzhou, China"]},{"name":"Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun-ru","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, School of Health, Fujian Medical University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"102290","DOI":"10.1016\/j.bspc.2020.102290","article-title":"A motion capture algorithm based on inertia-Kinect sensors for lower body elements and step length estimation","volume":"64","author":"Abbasi","year":"2021","journal-title":"Biomed. 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