{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:14:25Z","timestamp":1783314865945,"version":"3.54.6"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"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. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Assessing functional movements is important for evaluating shoulder impairments, as these movements directly reflect patients\u2019 capacity to perform daily activities. Intelligent rotator cuff injury (RCI) recognition is beneficial for clinical management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study aimed to develop accurate and cost-effective RCI recognition models by integrating machine learning (ML)\/deep learning (DL) algorithms with upper limb kinematic data collected in functional movement, and to explore optimal motion task combinations and model configurations for clinical practice. A total of 102 participants were prospectively enrolled, comprising 51 patients diagnosed with RCI, 25 patients diagnosed with adhesive capsulitis and 26 healthy volunteers. Patients with adhesive capsulitis were included to assess model\u2019s ability to distinguish RCI from other shoulder disorders that present with similar function limitations. Each participant performed six functional movements simulating daily activities and two shoulder range of motion (ROM) tests. Upper limb kinematic data were collected by inertial measurement units (IMUs) and analyzed via eight classification models, including the proposed DL based RCI recognition model (RCIRNet) and seven conventional ML models (e.g., KNN, SVM, DT, RF, NB, AdaBoost, and XGBoost). Given the relatively small sample size, data augmentation was applied to mitigate overfitting and improve model generalization. Model performance was evaluated across single and combined motion tasks via Accuracy, Precision, Recall, F1-score, and AUC with its 95% confidence interval (CI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Results demonstrated that shoulder frontal ROM test alone achieved the best performance with RCIRNet, yielding an Accuracy of 0.89, F1-score of 0.89, and AUC of 0.93 (95%CI, 0.88\u20130.98). Combining four tasks increased Recall to 1.0 with high level Accuracy, F1-score, and AUC. However, adding more movements (5\u20138 tasks) did not improve model performance. Among different model configurations, RCIRNet performed best, SVM demonstrated overall stability, and KNN excelled in Recall.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>It is concluded that selecting discriminant movements is more critical for effective RCI recognition than simply increasing the quantity of movement tasks. The shoulder frontal ROM test combined with RCIRNet provides an optimal approach for accurate and efficient clinical screening and rehabilitation monitoring.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2026.1794238","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T04:20:57Z","timestamp":1783311657000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["IMU-based rotator cuff injury recognition with varying configurations and combinations"],"prefix":"10.3389","volume":"17","author":[{"given":"Wanwan","family":"Xiong","sequence":"first","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianwu","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianning","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, The First Affiliated Hospital, Fujian Medical University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuexin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun","family":"Pei","sequence":"additional","affiliation":[{"name":"School of Medicine and Health Science, Guangzhou Huashang College","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Hao","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Transportation Engineering, Xiamen City University","place":["Xiamen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, The First Affiliated Hospital, Fujian Medical University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunru","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":[[2026,7,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"WIREs Comput. Stat."},{"key":"B2","doi-asserted-by":"publisher","first-page":"109920","DOI":"10.1016\/j.gaitpost.2025.08.022","article-title":"A benchmark analysis of fall detection algorithms with chest-and wrist-worn IMUs on public datasets","volume":"122","author":"Audisio","year":"2025","journal-title":"Gait Posture"},{"key":"B3","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.1109\/TBME.2011.2179545","article-title":"A robust and sensitive metric for quantifying movement smoothness","volume":"59","author":"Balasubramanian","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1177\/2055668320915377","article-title":"Segmentation of shoulder rehabilitation exercises for single and multiple inertial sensor systems","volume":"7","author":"Brennan","year":"2020","journal-title":"J. Rehabil. Assist. Technol. Eng."},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.3390\/s23229264","article-title":"Assessment of an IMU-based experimental set-up for upper limb motion in obese subjects","volume":"23","author":"Cerfoglio","year":"2023","journal-title":"Sensors"},{"key":"B6","doi-asserted-by":"publisher","first-page":"115","DOI":"10.4103\/jmedsci.jmedsci_89_21","article-title":"Inertial measurement unit-based functional evaluation for adhesive capsulitis assessment","volume":"42","author":"Chang","year":"2022","journal-title":"J. Med. Sci."},{"key":"B7","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.gaitpost.2024.09.002","article-title":"Comparative analysis of upper body kinematics in stroke, Parkinson\u2019s disease, and healthy subjects: An observational study using IMU-based targeted box and block test","volume":"114","author":"Cocco","year":"2024","journal-title":"Gait Posture"},{"key":"B8","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1016\/j.clinbiomech.2014.06.013","article-title":"Quantitative assessment based on kinematic measures of functional impairments during upper extremity movements: A review","volume":"29","author":"de los Reyes-Guzm\u00e1n","year":"2014","journal-title":"Clin. Biomech."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1919","DOI":"10.1177\/0363546518768276","article-title":"Relationship between deltoid and rotator cuff muscles during dynamic shoulder abduction: A biomechanical study of rotator cuff tear progression","volume":"46","author":"Dyrna","year":"2018","journal-title":"Am. J. Sports Med."},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e15804","article-title":"Detection of the rotator cuff tears using a novel convolutional neural network from magnetic resonance image (MRI)","volume":"9","author":"Esfandiari","year":"2023","journal-title":"Heliyon"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1578","DOI":"10.1016\/j.jbiomech.2009.03.049","article-title":"IMU: Inertial sensing of vertical CoM movement","volume":"42","author":"Esser","year":"2009","journal-title":"J. Biomech."},{"key":"B12","first-page":"1","article-title":"Shoulder rotator cuff tear detection from ultrasound videos using deep reinforcement learning","author":"Ghosh","year":"2025"},{"key":"B13","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1186\/s13018-023-03909-z","article-title":"Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears","volume":"18","author":"Guo","year":"2023","journal-title":"J. Orthop. Surg. Res."},{"key":"B14","doi-asserted-by":"publisher","first-page":"1733","DOI":"10.1016\/j.jse.2023.12.009","article-title":"Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning","volume":"33","author":"Hashimoto","year":"2024","journal-title":"J. Shoulder Elbow Surg."},{"key":"B15","doi-asserted-by":"publisher","first-page":"1269","DOI":"10.1007\/s11517-022-02502-6","article-title":"Classification of rotator cuff tears in ultrasound images using deep learning models","volume":"60","author":"Ho","year":"2022","journal-title":"Med. Biol. Eng. Comput."},{"key":"B16","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1016\/j.jos.2023.05.004","article-title":"Deep learning-based screening tool for rotator cuff tears on shoulder radiography","volume":"29","author":"Iio","year":"2024","journal-title":"J. Orthopaedic Sci."},{"key":"B17","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.gaitpost.2023.11.002","article-title":"Measuring highly accurate foot position and angle trajectories with foot-mounted IMUs in clinical practice","volume":"108","author":"Jocham","year":"2024","journal-title":"Gait Posture"},{"key":"B18","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.shaw.2025.01.003","article-title":"Prediction of work-relatedness of shoulder musculoskeletal disorders as by using machine learning","volume":"16","author":"Jung","year":"2025","journal-title":"Saf. Health Work"},{"key":"B19","doi-asserted-by":"publisher","first-page":"6119","DOI":"10.1080\/09638288.2021.1957027","article-title":"The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review","volume":"44","author":"Kim","year":"2022","journal-title":"Disabil. Rehabil."},{"key":"B20","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.jse.2019.07.038","article-title":"Motion quality in rotator cuff tear using an inertial measurement unit: new parameters for dynamic motion assessment","volume":"29","author":"Kwak","year":"2020","journal-title":"J. Shoulder Elbow Surg."},{"key":"B21","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s12984-024-01343-8","article-title":"Deep learning model for classifying shoulder pain rehabilitation exercises using IMU sensor","volume":"21","author":"Lee","year":"2024","journal-title":"J. NeuroEng. Rehabil."},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0118642","article-title":"Recognizing complex upper extremity activities using body worn sensors","volume":"10","author":"Lemmens","year":"2015","journal-title":"PloS One"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering12030218","article-title":"Multi-model segmentation algorithm for rotator cuff injury based on MRI images","volume":"12","author":"Li","year":"2025","journal-title":"Bioengineering"},{"key":"B26","doi-asserted-by":"publisher","first-page":"3421","DOI":"10.1109\/TNSRE.2025.3603555","article-title":"Low-cost IMU-based system for automated Parkinson\u2019s subtype and stage classification to support precision rehabilitation","volume":"33","author":"Liu","year":"2025","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"B24","first-page":"1","article-title":"Wearable-based frozen shoulder rehabilitation exercise recognition using machine learning approaches","author":"Liu","year":"2023"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.3390\/s24206656","article-title":"Inertial measurement unit-based frozen shoulder identification from daily shoulder tasks using machine learning approaches","volume":"24","author":"Liu","year":"2024","journal-title":"Sensors"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15111315","article-title":"Artificial intelligence in rotator cuff tear detection: A systematic review of MRI-based models","volume":"15","author":"Longo","year":"2025","journal-title":"Diagnostics"},{"key":"B28","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1080\/10749357.2023.2197278","article-title":"Upper limb assessment with inertial measurement units according to the international classification of functioning in stroke: a systematic review and correlation meta-analysis","volume":"31","author":"Martino Cinnera","year":"2024","journal-title":"Top. Stroke Rehabil."},{"key":"B29","volume-title":"Rotator Cuff Injury","author":"May","year":"2025"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2020.558771","article-title":"Estimating movement smoothness from inertial measurement units","volume":"8","author":"Melendez-Calderon","year":"2021","journal-title":"Front. Bioeng. Biotechnol."},{"key":"B31","article-title":"Frozen Shoulder (Archived)","author":"Mezian","year":"2026","journal-title":"StatPearls"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinbiomech.2021.105494","article-title":"Glenohumeral joint loading is impacted by rotator cuff tear severity during functional task performance","volume":"90","author":"Pataky","year":"2021","journal-title":"Clin. Biomech."},{"key":"B33","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1088\/0967-3334\/36\/3\/547","article-title":"Enhancing clinically-relevant shoulder function assessment using only essential movements","volume":"36","author":"Pichonnaz","year":"2015","journal-title":"Physiol. Meas."},{"key":"B34","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1007\/s44174-025-00406-1","article-title":"Machine learning-based analysis of surface electromyography for performance assessment in speech therapy","volume":"4","author":"Renuka","year":"2025","journal-title":"Biomed. Materials Devices"},{"key":"B35","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1136\/bjsports-2014-094148","article-title":"Diagnostic accuracy of ultrasonography, MRI and MR arthrography in the characterisation of rotator cuff disorders: a systematic review and meta-analysis","volume":"49","author":"Roy","year":"2015","journal-title":"Br. J. Sports Med."},{"key":"B36","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1097\/PHM.0b013e3181ae0733","article-title":"Three-dimensional shoulder kinematics to complete activities of daily living","volume":"88","author":"Rundquist","year":"2009","journal-title":"Am. J. Phys. Med. Rehabil."},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1177\/2325967120964476","article-title":"Biomechanics in an incomplete versus complete supraspinatus tear: A cadaveric study","volume":"8","author":"Rybalko","year":"2020","journal-title":"Orthop. J. Sports Med."},{"key":"B38","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","article-title":"Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"B39","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1002\/ksa.12431","article-title":"Machine-learning models for shoulder rehabilitation exercises classification using a wearable system","volume":"33","author":"Sassi","year":"2025","journal-title":"Knee Surgery Sports Traumatology Arthroscopy"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.3390\/s24092936","article-title":"Advanced home-based shoulder rehabilitation: A systematic review of remote monitoring devices and their therapeutic efficacy","volume":"24","author":"Sassi","year":"2024","journal-title":"Sensors"},{"key":"B41","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1186\/s12891-021-04659-x","article-title":"Interrater reliability of physical examination tests in the acute phase of shoulder injuries","volume":"22","author":"Schmidt","year":"2021","journal-title":"BMC Musculoskelet. Disord."},{"key":"B42","doi-asserted-by":"publisher","first-page":"102331","DOI":"10.1016\/j.jelekin.2019.07.001","article-title":"Shoulder complex kinematics pre- and post- rotator cuff repair","volume":"62","author":"Schnorenberg","year":"2022","journal-title":"J. Electromyography Kinesiology"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.2196\/60521","article-title":"The use of artificial intelligence and wearable inertial measurement units in medicine: Systematic review","volume":"13","author":"Smits Serena","year":"2025","journal-title":"JMIR Mhealth Uhealth"},{"key":"B44","doi-asserted-by":"publisher","first-page":"12638","DOI":"10.1038\/s41598-023-39862-4","article-title":"Classification of mild Parkinson\u2019s disease: data augmentation of time-series gait data obtained via inertial measurement units","volume":"13","author":"Uchitomi","year":"2023","journal-title":"Sci. Rep."},{"key":"B45","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1097\/CORR.0000000000001555","article-title":"Compensatory movement patterns are based on abnormal activity of the biceps brachii and posterior deltoid muscles in patients with symptomatic rotator cuff tears","volume":"479","author":"Veen","year":"2021","journal-title":"Clin. Orthop. Relat. Res."},{"key":"B46","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1016\/j.jbiomech.2016.01.029","article-title":"The effects of a rotator cuff tear on activities of daily living in older adults: A kinematic analysis","volume":"49","author":"Vidt","year":"2016","journal-title":"J. Biomech."},{"key":"B47","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.clinbiomech.2018.10.004","article-title":"Modeling a rotator cuff tear: Individualized shoulder muscle forces influence glenohumeral joint contact force predictions","volume":"60","author":"Vidt","year":"2018","journal-title":"Clin. Biomech."},{"key":"B48","doi-asserted-by":"publisher","first-page":"12924","DOI":"10.1109\/TII.2024.3431018","article-title":"Wavelet encoding network for inertial signal enhancement via feature supervision","volume":"20","author":"Wang","year":"2024","journal-title":"IEEE Trans. Industr. Inform."},{"key":"B49","doi-asserted-by":"publisher","first-page":"2708","DOI":"10.1016\/j.arthro.2024.12.024","article-title":"Diagnostic accuracy and interobserver reliability of rotator cuff tear detection with ultrasonography are improved with attentional deep learning","volume":"41","author":"Wu","year":"2025","journal-title":"Arthroscopy: J. Arthroscopic Related Surg."},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1016\/j.slast.2024.100149","article-title":"Machine-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint","volume":"29","author":"Zhao","year":"2024","journal-title":"SLAS Technol."}],"container-title":["Frontiers in Physiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2026.1794238\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T04:21:00Z","timestamp":1783311660000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2026.1794238\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,6]]},"references-count":50,"alternative-id":["10.3389\/fphys.2026.1794238"],"URL":"https:\/\/doi.org\/10.3389\/fphys.2026.1794238","relation":{},"ISSN":["1664-042X"],"issn-type":[{"value":"1664-042X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,6]]},"article-number":"1794238"}}