{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T10:48:45Z","timestamp":1791888525477,"version":"4.3.4"},"reference-count":33,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Quantitative upper-limb assessment in rehabilitation traditionally relies on expensive optoelectronic reference tracking. While single-camera markerless systems offer an accessible alternative, their viewpoint-dependent accuracy and depth ambiguity remain critical concerns. This study systematically validates an integrated MediaPipe tracking pipeline (Pose and Hands) against an 8-camera optoelectronic system. Ten healthy males performed four clinical upper-limb tasks, recorded simultaneously across three spatial camera viewpoints (frontal, oblique elevated, and oblique side). To isolate spatial accuracy, validations utilized direct unmodeled geometric tracking vectors rather than clinical joint models. Results demonstrate that agreement and accuracy are systematically dependent on movement geometry and camera perspective. Planar tasks like shoulder abduction exhibited moderate to good peak range-of-motion agreement (ICC = 0.63 \u2212 0.82) and precision (RMSE = 9.00\u2218 \u2212 11.23\u2218). For transverse plane movements like shoulder rotation, an oblique elevated perspective improved peak range-of-motion agreement (ICC = 0.68). Out-of-plane tracking and fine distal rotations (forearm pronation) exhibited poor agreement (ICC \u2264 0.34) and substantial trajectory instability (RMSE &gt; 30\u2218). Ultimately, while single-camera monocular RGB tracking provides sufficient validity for gross range-of-motion screening during planar upper-limb tasks, quantitative assessment of multi-planar and distal rotational kinematics requires multi-view camera fusion, depth sensing, or specialized 2D shape-regression architectures.<\/jats:p>","DOI":"10.3390\/s26196248","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T08:15:18Z","timestamp":1790928918000},"page":"6248","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Concurrent Validity and Agreement of Viewpoint-Dependent Markerless Motion Tracking in Upper-Limb Assessment: A Validation Against Optoelectronic Motion Capture"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8965-234X","authenticated-orcid":false,"given":"Suman","family":"Shrestha","sequence":"first","affiliation":[{"name":"Graduate School of Engineering, Osaka Sangyo University, Daito 574-8530, Osaka, Japan"},{"name":"Department of Research and Development, The Hyogo Institute of Assistive Technology, Kobe 651-2181, Hyogo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Yuichiro","family":"Honda","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering, Faculty of Systems Engineering, Osaka Sangyo University, Daito 574-8530, Osaka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8908-1561","authenticated-orcid":false,"given":"Haruki","family":"Toda","sequence":"additional","affiliation":[{"name":"Department of Research and Development, The Hyogo Institute of Assistive Technology, Kobe 651-2181, Hyogo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"given":"Takaaki","family":"Chin","sequence":"additional","affiliation":[{"name":"Department of Research and Development, The Hyogo Institute of Assistive Technology, Kobe 651-2181, Hyogo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"given":"Mitsuru","family":"Irie","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Osaka Sangyo University, Daito 574-8530, Osaka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.jmpt.2020.12.001","article-title":"Inclinometer Reliability for Shoulder Ranges of Motion in Individuals With Subacromial Impingement Syndrome","volume":"44","author":"Tozzo","year":"2021","journal-title":"J. Manip. Physiol. Ther."},{"key":"ref_2","first-page":"100385","article-title":"Test-Retest Reliability and Minimal Detectable Change of Body Function Impairment and Activity Limitation Outcomes Using a Mechatronic Evaluation Designed for Inpatient Stroke Research","volume":"7","author":"Gyarmaty","year":"2025","journal-title":"Arch. Rehabil. Res. Clin. Transl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Francia, C., Donno, L., Motta, F., Cimolin, V., Galli, M., and LoMauro, A. (2026). Accuracy and Reliability of Markerless Human Pose Estimation for Upper Limb Kinematic Analysis Across Full and Partial Range of Motion Tasks. Appl. Sci., 16.","DOI":"10.3390\/app16031202"},{"key":"ref_4","first-page":"173","article-title":"Reliability of Different Protocols for Elbow Flexion and Extension Strength Measurement via Hand-held Dynamometer","volume":"39","author":"Karabay","year":"2025","journal-title":"Sport. Sportschaden"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kusunose, M., Inui, A., Mifune, Y., Yamaura, K., Shinohara, I., Tanaka, S., Ehara, Y., Takigami, S., Osawa, S., and Nakabayashi, D. (2025). Estimation of Forearm Pronation\u2013Supination Angles Using MediaPipe and IMU Sensors: Performance Comparison and Interpretability Analysis of Machine Learning Models. Appl. Sci., 15.","DOI":"10.3390\/app151910527"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lee, U., Lee, S., Kim, S.-A., Kim, Y., and Lee, S. (2025). Validity and Reliability of Single Camera Markerless Motion Capture Systems with RGB-D Sensors for Measuring Shoulder Range-of-Motion: A Systematic Review. Front. Bioeng. Biotechnol., 13.","DOI":"10.3389\/fbioe.2025.1570637"},{"key":"ref_7","first-page":"e57228","article-title":"Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-Extremity Impairments","volume":"133","author":"Murphy","year":"2018","journal-title":"J. Vis. Exp."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"131","DOI":"10.5136\/lifesupport.33.131","article-title":"Robust Camera Placement for Upper Limb Movement Measurement of Upper Limb Prosthesis Operation Evaluation with an Optical Motion Capture","volume":"33","author":"Takami","year":"2021","journal-title":"J. Life Support Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111751","DOI":"10.1016\/j.jbiomech.2023.111751","article-title":"Markerless Motion Capture Estimates of Lower Extremity Kinematics and Kinetics Are Comparable to Marker-Based across 8 Movements","volume":"157","author":"Song","year":"2023","journal-title":"J. Biomech."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hardy, P., and Kim, H. (2024, January 17\u201318). Unsupervised Multi-Person 3D Human Pose Estimation From 2D Poses Alone. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW63382.2024.00462"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6628320","DOI":"10.1155\/2021\/6628320","article-title":"Inertial-Based Human Motion Capture: A Technical Summary of Current Processing Methodologies for Spatiotemporal and Kinematic Measures","volume":"2021","author":"Hindle","year":"2021","journal-title":"Appl. Bionics Biomech."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ortigas V\u00e1squez, A., Maas, A., List, R., Sch\u00fctz, P., Taylor, W.R., and Grupp, T.M. (2022). A Framework for Analytical Validation of Inertial-Sensor-Based Knee Kinematics Using a Six-Degrees-of-Freedom Joint Simulator. Sensors, 23.","DOI":"10.3390\/s23010348"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Teufl, W., Miezal, M., Taetz, B., Fr\u00f6hlich, M., and Bleser, G. (2019). Validity of Inertial Sensor Based 3D Joint Kinematics of Static and Dynamic Sport and Physiotherapy Specific Movements. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0213064"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3433","DOI":"10.3233\/THC-240202","article-title":"Using Markerless Motion Capture and Musculoskeletal Models: An Evaluation of Joint Kinematics","volume":"32","author":"Auer","year":"2024","journal-title":"Technol. Health Care"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Foreman, M.H., and Engsberg, J.R. (2020). The Validity and Reliability of the Microsoft Kinect for Measuring Trunk Compensation during Reaching. Sensors, 20.","DOI":"10.3390\/s20247073"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100128","DOI":"10.1016\/j.bea.2024.100128","article-title":"Validation of Upper Extremity Kinematics Using Markerless Motion Capture","volume":"7","author":"Hansen","year":"2024","journal-title":"Biomed. Eng. Adv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Garc\u00eda, V., and Santos, O.C. (2025). Towards Intelligent Assessment in Personalized Physiotherapy with Computer Vision. Sensors, 25.","DOI":"10.3390\/s25113436"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yan, X., Zhang, L., Liu, B., and Qu, G. (2025). Efficient Limb Range of Motion Analysis from a Monocular Camera for Edge Devices. Sensors, 25.","DOI":"10.3390\/s25030627"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pavllo, D., Feichtenhofer, C., Grangier, D., and Auli, M. (2019). 3D Human Pose Estimation in Video With Temporal Convolutions and Semi-Supervised Training. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.00794"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3603618","article-title":"Deep Learning-Based Human Pose Estimation: A Survey","volume":"56","author":"Zheng","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Maji, D., Nagori, S., Mathew, M., and Poddar, D. (2022). YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE.","DOI":"10.1109\/CVPRW56347.2022.00297"},{"key":"ref_22","unstructured":"Bazarevsky, V., Grishchenko, I., Raveendran, K., Zhu, T., Zhang, F., and Grundmann, M. (2020). BlazePose: On-Device Real-Time Body Pose Tracking. arXiv."},{"key":"ref_23","unstructured":"Zhang, F., Bazarevsky, V., Vakunov, A., Tkachenka, A., Sung, G., Chang, C.-L., and Grundmann, M. (2020). MediaPipe Hands: On-Device Real-Time Hand Tracking. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shrestha, S., Takami, H., Honda, Y., and Irie, M. (2024). Research on Measurement System of Upper Limb by Using Single Monocular Web Camera and Inference AI. Proceedings of the 2024 17th International Convention on Rehabilitation Engineering and Assistive Technology (i-CREATe), IEEE.","DOI":"10.1109\/i-CREATe62067.2024.10776555"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nakano, N., Sakura, T., Ueda, K., Omura, L., Kimura, A., Iino, Y., Fukashiro, S., and Yoshioka, S. (2020). Evaluation of 3D Markerless Motion Capture Accuracy Using OpenPose With Multiple Video Cameras. Front. Sports Act. Living, 2.","DOI":"10.3389\/fspor.2020.00050"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1515\/cdbme-2023-1141","article-title":"Accuracy Evaluation of 3D Pose Estimation with MediaPipe Pose for Physical Exercises","volume":"9","author":"Dill","year":"2023","journal-title":"Curr. Dir. Biomed. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"72650","DOI":"10.1109\/ACCESS.2024.3399222","article-title":"Efficient Monocular Human Pose Estimation Based on Deep Learning Methods: A Survey","volume":"12","author":"Yan","year":"2024","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Balci, I.C., Sayin, I., Salturk, S., Gursoy, R., Ozsoy, U., Dogru, H.C., Akca, G., Eraslan, A., Sahin, O., and Demircali, A.A. (2026). Reliability Assessment of Markerless Technologies in Biomechanical Motion Analysis: A Performance Comparison. Front. Sports Act. Living, 7.","DOI":"10.3389\/fspor.2025.1712332"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Carnevale, A., Mancini, L., Longo, U.G., Schena, E., Papalia, R., Umbrico, A., Orlandini, A., and Tamantini, C. (2026). Viewpoint Effects on Markerless Upper Limb Kinematics During Musculoskeletal Rehabilitation Exercises. Proceedings of the 2026 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT), IEEE.","DOI":"10.1109\/MetroInd4.0IoT69397.2026.11653067"},{"key":"ref_30","unstructured":"(1974). The Japanese Orthopaedic Association Committee on Physical Disabilities; The Japanese Association of Rehabilitation Medicine Committee on Evaluation Criteria Joint. Range of Motion Display and Measurement Method. Jpn. J. Rehabil. Med., 11, 127\u2013132."},{"key":"ref_31","unstructured":"Machida, S., and Sakai, T. (2018). Anatomy: PT\/OT Visual Text Specialized Foundation, Yodosha."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research","volume":"15","author":"Koo","year":"2016","journal-title":"J. Chiropr. Med."},{"key":"ref_33","first-page":"98","article-title":"Evaluating Change in Clinical Status: Reliability and Measures of Agreement for the Assessment of Glenohumeral Range of Motion","volume":"5","author":"Muir","year":"2010","journal-title":"N. Am. J. Sports Phys. Ther."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6248\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T10:11:26Z","timestamp":1791886286000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6248"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,1]]},"references-count":33,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196248"],"URL":"https:\/\/doi.org\/10.3390\/s26196248","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,1]]}}}