{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T14:05:05Z","timestamp":1776261905869,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T00:00:00Z","timestamp":1645574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Industrial Research Fund of the University of Antwerp","award":["42602"],"award-info":[{"award-number":["42602"]}]},{"name":"Industrial Research Fund of the University of Antwerp","award":["36536"],"award-info":[{"award-number":["36536"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To automatically evaluate the ergonomics of workers, 3D skeletons are needed. Most ergonomic assessment methods, like REBA, are based on the different 3D joint angles. Thanks to the huge amount of training data, 2D skeleton detectors have become very accurate. In this work, we test three methods to calculate 3D skeletons from 2D detections: using the depth from a single RealSense range camera, triangulating the joints using multiple cameras, and combining the triangulation of multiple camera pairs. We tested the methods using recordings of a person doing different assembly tasks. We compared the resulting joint angles to the ground truth of a VICON marker-based tracking system. The resulting RMS angle error for the triangulation methods is between 12\u00b0 and 16\u00b0, showing that they are accurate enough to calculate a useful ergonomic score from.<\/jats:p>","DOI":"10.3390\/s22051729","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:53:26Z","timestamp":1645664006000},"page":"1729","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Accuracy Assessment of Joint Angles Estimated from 2D and 3D Camera Measurements"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-7105-4829","authenticated-orcid":false,"given":"Izaak","family":"Van Crombrugge","sequence":"first","affiliation":[{"name":"Faculty of Applied Engineering Department Electromechanics, Universiteit Antwerpen, Groenenborgerlaan 171, 2020 Antwerpen, Belgium"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-0590-2770","authenticated-orcid":false,"given":"Seppe","family":"Sels","sequence":"additional","affiliation":[{"name":"Faculty of Applied Engineering Department Electromechanics, Universiteit Antwerpen, Groenenborgerlaan 171, 2020 Antwerpen, Belgium"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-5882-2531","authenticated-orcid":false,"given":"Bart","family":"Ribbens","sequence":"additional","affiliation":[{"name":"Faculty of Applied Engineering Department Electromechanics, Universiteit Antwerpen, Groenenborgerlaan 171, 2020 Antwerpen, Belgium"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-9944-520X","authenticated-orcid":false,"given":"Gunther","family":"Steenackers","sequence":"additional","affiliation":[{"name":"Faculty of Applied Engineering Department Electromechanics, Universiteit Antwerpen, Groenenborgerlaan 171, 2020 Antwerpen, Belgium"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-0921-1950","authenticated-orcid":false,"given":"Rudi","family":"Penne","sequence":"additional","affiliation":[{"name":"Faculty of Applied Engineering Department Electromechanics, Universiteit Antwerpen, Groenenborgerlaan 171, 2020 Antwerpen, Belgium"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-7975-1338","authenticated-orcid":false,"given":"Steve","family":"Vanlanduit","sequence":"additional","affiliation":[{"name":"Faculty of Applied Engineering Department Electromechanics, Universiteit Antwerpen, Groenenborgerlaan 171, 2020 Antwerpen, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102882","DOI":"10.1016\/j.apergo.2019.102882","article-title":"Ergonomics assessment methods used by ergonomics professionals","volume":"81","author":"Lowe","year":"2019","journal-title":"Appl. Ergon."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/0003-6870(93)90080-S","article-title":"RULA: A survey method for the investigation of work-related upper limb disorders","volume":"24","author":"McAtamney","year":"1993","journal-title":"Appl. Ergon."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/S0003-6870(99)00039-3","article-title":"Rapid Entire Body Assessment (REBA)","volume":"31","author":"Hignett","year":"2000","journal-title":"Appl. Ergon."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/0003-6870(77)90164-8","article-title":"Correcting working postures in industry: A practical method for analysis","volume":"8","author":"Karhu","year":"1977","journal-title":"Appl. Ergon."},{"key":"ref_5","unstructured":"Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., and Girshick, R. (2021, March 01). Detectron2. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/github.com\/facebookresearch\/detectron2."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Microsoft COCO: Common Objects in Context. Computer Vision\u2014ECCV 2014, Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Pishchulin, L., Gehler, P., and Schiele, B. (2014, January 23\u201328). 2D Human Pose Estimation: New Benchmark and State of the Art Analysis. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.471"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102897","DOI":"10.1016\/j.cviu.2019.102897","article-title":"Monocular human pose estimation: A survey of deep learning-based methods","volume":"192","author":"Chen","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1006\/cviu.2000.0897","article-title":"A Survey of Computer Vision-Based Human Motion Capture","volume":"81","author":"Moeslund","year":"2001","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, T., An, D., Jia, Y., and Yue, Y. (2021). A Review: Point Cloud-Based 3D Human Joints Estimation. Sensors, 21.","DOI":"10.3390\/s21051684"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A. (2011, January 20\u201325). Real-time human pose recognition in parts from single depth images. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995316"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.E., and Sheikh, Y. (2017, January 21\u201326). Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.143"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107534","DOI":"10.1016\/j.patcog.2020.107534","article-title":"Single-shot 3D multi-person pose estimation in complex images","volume":"112","author":"Benzine","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, C.H., and Ramanan, D. (2017, January 21\u201326). 3D Human Pose Estimation = 2D Pose Estimation + Matching. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.610"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"35947","DOI":"10.1109\/ACCESS.2021.3062426","article-title":"3D Human Pose Estimation With Spatial Structure Information","volume":"9","author":"Huang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1109\/TPAMI.2015.2509986","article-title":"3D Pictorial Structures Revisited: Multiple Human Pose Estimation","volume":"38","author":"Belagiannis","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Amin, S., Andriluka, M., Rohrbach, M., and Schiele, B. (2013, January 9\u201313). Multi-view Pictorial Structures for 3D Human Pose Estimation. Proceedings of the British Machine Vision Conference 2013. British Machine Vision Association, Bristol, UK.","DOI":"10.5244\/C.27.45"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2004). Multiple View Geometry in Computer Vision, Cambridge University Press.","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_19","unstructured":"Sunday, D. (2021). Practical Geometry Algorithms: With C++ Code, Amazon Digital Services LLC. KDP Print US."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dong, J., Jiang, W., Huang, Q., Bao, H., and Zhou, X. (2019, January 15\u201320). Fast and Robust Multi-Person 3D Pose Estimation From Multiple Views. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00798"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., and Sun, J. (2018, January 18\u201323). Cascaded Pyramid Network for Multi-person Pose Estimation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00742"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s00138-020-01120-2","article-title":"A generalizable approach for multi-view 3D human pose regression","volume":"32","author":"Kadkhodamohammadi","year":"2021","journal-title":"Mach. Vis. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Iskakov, K., Burkov, E., Lempitsky, V., and Malkov, Y. (2019, January 27\u201328). Learnable Triangulation of Human Pose. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00781"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s41095-020-0171-y","article-title":"3D hypothesis clustering for cross-view matching in multi-person motion capture","volume":"6","author":"Li","year":"2020","journal-title":"Comput. Vis. Media"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Elhayek, A., de Aguiar, E., Jain, A., Tompson, J., Pishchulin, L., Andriluka, M., Bregler, C., Schiele, B., and Theobalt, C. (2015, January 7\u201312). Efficient ConvNet-based marker-less motion capture in general scenes with a low number of cameras. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299005"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2461","DOI":"10.1007\/s11042-020-09733-5","article-title":"Uncalibrated multi-view multiple humans association and 3D pose estimation by adversarial learning","volume":"80","author":"Kasaei","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ferrari, V., Marin-Jimenez, M., and Zisserman, A. (2008, January 24\u201326). Progressive search space reduction for human pose estimation. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AL, USA.","DOI":"10.1109\/CVPR.2008.4587468"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2878","DOI":"10.1109\/TPAMI.2012.261","article-title":"Articulated Human Detection with Flexible Mixtures of Parts","volume":"35","author":"Yang","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1080\/19346182.2014.968165","article-title":"The accuracy of the Microsoft Kinect in joint angle measurement","volume":"7","author":"Choppin","year":"2014","journal-title":"Sports Technol."},{"key":"ref_30","unstructured":"Cao, Z., Simon, T., Wei, S.E., and Sheikh, Y. (2021, January 01). OpenPose Documentation: Pose Output Format (COCO). Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/cmu-perceptual-computing-lab.github.io\/openpose\/web\/html\/doc\/md_doc_02_output."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106305","DOI":"10.1016\/j.optlaseng.2020.106305","article-title":"Extrinsic camera calibration for non-overlapping cameras with Gray code projection","volume":"134","author":"Penne","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Van Crombrugge, I., Penne, R., and Vanlanduit, S. (2021). Extrinsic Camera Calibration with Line-Laser Projection. Sensors, 21.","DOI":"10.3390\/s21041091"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.3390\/s150101785","article-title":"Pose Estimation with a Kinect for Ergonomic Studies: Evaluation of the Accuracy Using a Virtual Mannequin","volume":"15","author":"Plantard","year":"2015","journal-title":"Sensors"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.apergo.2017.04.004","article-title":"Using the Microsoft Kinect\u2122 to assess 3-D shoulder kinematics during computer use","volume":"65","author":"Xu","year":"2017","journal-title":"Appl. Ergon."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.apergo.2019.05.004","article-title":"RGB-D ergonomic assessment system of adopted working postures","volume":"80","author":"Abobakr","year":"2019","journal-title":"Appl. Ergon."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhang, R., Lee, C.H., and Lee, Y.C. (2020). An Evaluation of Posture Recognition Based on Intelligent Rapid Entire Body Assessment System for Determining Musculoskeletal Disorders. Sensors, 20.","DOI":"10.3390\/s20164414"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/22\/5\/1729\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:25:51Z","timestamp":1760135151000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/22\/5\/1729"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,23]]},"references-count":36,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22051729"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/s22051729","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,23]]}}}