{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T04:55:45Z","timestamp":1778648145489,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T00:00:00Z","timestamp":1646352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007366","name":"Fondazione Italiana Sclerosi Multipla","doi-asserted-by":"publisher","award":["FISM - 2019\/PR-single050"],"award-info":[{"award-number":["FISM - 2019\/PR-single050"]}],"id":[{"id":"10.13039\/100007366","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The analysis of human gait is an important tool in medicine and rehabilitation to evaluate the effects and the progression of neurological diseases resulting in neuromotor disorders. In these fields, the gold standard techniques adopted to perform gait analysis rely on motion capture systems and markers. However, these systems present drawbacks: they are expensive, time consuming and they can affect the naturalness of the motion. For these reasons, in the last few years, considerable effort has been spent to study and implement markerless systems based on videography for gait analysis. Unfortunately, only few studies quantitatively compare the differences between markerless and marker-based systems in 3D settings. This work presented a new RGB video-based markerless system leveraging computer vision and deep learning to perform 3D gait analysis. These results were compared with those obtained by a marker-based motion capture system. To this end, we acquired simultaneously with the two systems a multimodal dataset of 16 people repeatedly walking in an indoor environment. With the two methods we obtained similar spatio-temporal parameters. The joint angles were comparable, except for a slight underestimation of the maximum flexion for ankle and knee angles. Taking together these results highlighted the possibility to adopt markerless technique for gait analysis.<\/jats:p>","DOI":"10.3390\/s22052011","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:40:02Z","timestamp":1646599202000},"page":"2011","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":107,"title":["Markerless vs. Marker-Based Gait Analysis: A Proof of Concept Study"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8644-0175","authenticated-orcid":false,"given":"Matteo","family":"Moro","sequence":"first","affiliation":[{"name":"Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genova, Italy"},{"name":"Machine Learning Genoa (MaLGa) Center, 16146 Genova, Italy"},{"name":"Spinal Cord Italian Laboratory (S.C.I.L.), 17027 Pietra Ligure, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7864-3036","authenticated-orcid":false,"given":"Giorgia","family":"Marchesi","sequence":"additional","affiliation":[{"name":"Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genova, Italy"},{"name":"Spinal Cord Italian Laboratory (S.C.I.L.), 17027 Pietra Ligure, Italy"}]},{"given":"Filip","family":"Hesse","sequence":"additional","affiliation":[{"name":"Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genova, Italy"}]},{"given":"Francesca","family":"Odone","sequence":"additional","affiliation":[{"name":"Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genova, Italy"},{"name":"Machine Learning Genoa (MaLGa) Center, 16146 Genova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2338-8995","authenticated-orcid":false,"given":"Maura","family":"Casadio","sequence":"additional","affiliation":[{"name":"Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genova, Italy"},{"name":"Spinal Cord Italian Laboratory (S.C.I.L.), 17027 Pietra Ligure, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1177\/1545968314532835","article-title":"The impact of dynamic balance measures on walking performance in multiple sclerosis","volume":"29","author":"Fritz","year":"2015","journal-title":"Neurorehabilit. Neural Repair"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"di Biase, L., Di Santo, A., Caminiti, M.L., De Liso, A., Shah, S.A., Ricci, L., and Di Lazzaro, V. (2020). Gait analysis in Parkinson\u2019s disease: An overview of the most accurate markers for diagnosis and symptoms monitoring. Sensors, 20.","DOI":"10.3390\/s20123529"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.gaitpost.2020.05.031","article-title":"Clinical efficacy of instrumented gait analysis: Systematic review 2020 update","volume":"80","author":"Wren","year":"2020","journal-title":"Gait Posture"},{"key":"ref_4","unstructured":"Whittle, M.W. (2014). Gait Analysis: An Introduction, Butterworth-Heinemann."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cloete, T., and Scheffer, C. (2008, January 20\u201325). Benchmarking of a full-body inertial motion capture system for clinical gait analysis. Proceedings of the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada.","DOI":"10.1109\/IEMBS.2008.4650232"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40798-018-0139-y","article-title":"A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system","volume":"4","author":"Colyer","year":"2018","journal-title":"Sport. Med.-Open"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.physio.2013.03.001","article-title":"Affordable clinical gait analysis: An assessment of the marker tracking accuracy of a new low-cost optical 3D motion analysis system","volume":"99","author":"Carse","year":"2013","journal-title":"Physiotherapy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103275","DOI":"10.1016\/j.cviu.2021.103275","article-title":"A review of 3D human pose estimation algorithms for markerless motion capture","volume":"212","author":"Desmarais","year":"2021","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Voulodimos, A., Doulamis, N., Doulamis, A., and Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Comput. Intell. Neurosci., 2018.","DOI":"10.1155\/2018\/7068349"},{"key":"ref_10","unstructured":"Zheng, C., Wu, W., Yang, T., Zhu, S., Chen, C., Liu, R., Shen, J., Kehtarnavaz, N., and Shah, M. (2020). Deep learning-based human pose estimation: A survey. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"32437","DOI":"10.1007\/s11042-019-07945-y","article-title":"Calibrated and synchronized multi-view video and motion capture dataset for evaluation of gait recognition","volume":"78","author":"Kwolek","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Moro, M., Casadio, M., Mrotek, L.A., Ranganathan, R., Scheidt, R., and Odone, F. (2021, January 19\u201322). On The Precision Of Markerless 3d Semantic Features: An Experimental Study On Violin Playing. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506356"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20673","DOI":"10.1038\/s41598-021-00212-x","article-title":"The accuracy of several pose estimation methods for 3D joint centre localisation","volume":"11","author":"Needham","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xiao, B., Wu, H., and Wei, Y. (2018, January 8\u201314). Simple baselines for human pose estimation and tracking. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01231-1_29"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s11263-020-01398-9","article-title":"AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild","volume":"129","author":"Zhang","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2003). Multiple View Geometry in Computer Vision, Cambridge University Press.","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","article-title":"Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments","volume":"36","author":"Ionescu","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TBME.2007.901024","article-title":"OpenSim: Open-source software to create and analyze dynamic simulations of movement","volume":"54","author":"Delp","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_19","unstructured":"Moro, M., Marchesi, G., Odone, F., and Casadio, M. (April, January 30). Markerless gait analysis in stroke survivors based on computer vision and deep learning: A pilot study. Proceedings of the 35th Annual ACM Symposium on Applied Computing, Brno, Czech Republic."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2629","DOI":"10.1007\/s11042-019-08275-9","article-title":"Human gait assessment using a 3D marker-less multimodal motion capture system","volume":"79","author":"Rodrigues","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1007\/s10439-006-9122-8","article-title":"A markerless motion capture system to study musculoskeletal biomechanics: Visual hull and simulated annealing approach","volume":"34","author":"Corazza","year":"2006","journal-title":"Ann. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Castelli, A., Paolini, G., Cereatti, A., and Della Croce, U. (2015). A 2D markerless gait analysis methodology: Validation on healthy subjects. Comput. Math. Methods Med., 2015.","DOI":"10.1155\/2015\/186780"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2722","DOI":"10.1016\/j.jbiomech.2013.08.011","article-title":"Concurrent validity of the Microsoft Kinect for assessment of spatiotemporal gait variables","volume":"46","author":"Clark","year":"2013","journal-title":"J. Biomech."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gabel, M., Gilad-Bachrach, R., Renshaw, E., and Schuster, A. (September, January 28). Full body gait analysis with Kinect. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6346340"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1142\/S021821300700345X","article-title":"Markerless human motion tracking from a single camera using interval particle filtering","volume":"16","author":"Saboune","year":"2007","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4054","DOI":"10.1038\/s41467-020-17807-z","article-title":"Deep neural networks enable quantitative movement analysis using single-camera videos","volume":"11","author":"Yang","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1113\/jphysiol.1996.sp021539","article-title":"Kinematic determinants of human locomotion","volume":"494","author":"Borghese","year":"1996","journal-title":"J. Physiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.gaitpost.2021.03.003","article-title":"A novel dataset and deep learning-based approach for marker-less motion capture during gait","volume":"86","author":"Vafadar","year":"2021","journal-title":"Gait Posture"},{"key":"ref_29","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 IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00781"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/0167-9457(91)90046-Z","article-title":"A gait analysis data collection and reduction technique","volume":"10","author":"Ounpuu","year":"1991","journal-title":"Hum. Mov. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","unstructured":"(2021, November 01). Motive: Optical Motion Capture Software. Available online: https:\/\/www.vicon.com\/."},{"key":"ref_33","unstructured":"(2021, November 01). Vicon. Available online: https:\/\/optitrack.com\/software\/motive\/."},{"key":"ref_34","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, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_35","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_36","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":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.gaitpost.2006.05.016","article-title":"Automatic detection of gait events using kinematic data","volume":"25","author":"Thorpe","year":"2007","journal-title":"Gait Posture"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1109\/TBME.2016.2586891","article-title":"Full-body musculoskeletal model for muscle-driven simulation of human gait","volume":"63","author":"Rajagopal","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1016\/j.jbiomech.2015.02.051","article-title":"Zero-vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis","volume":"48","author":"Pataky","year":"2015","journal-title":"J. Biomech."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Reddy, N.D., Guigues, L., Pishchulin, L., Eledath, J., and Narasimhan, S.G. (2021, January 20\u201325). TesseTrack: End-to-End Learnable Multi-Person Articulated 3D Pose Tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01494"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, Y., Yan, R., Fragkiadaki, K., and Yu, S.I. (2020, January 14\u201319). Epipolar transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00780"},{"key":"ref_42","unstructured":"Li, W., Liu, H., Ding, R., Liu, M., Wang, P., and Yang, W. (2021). Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Shan, W., Lu, H., Wang, S., Zhang, X., and Gao, W. (2021, January 20). Improving Robustness and Accuracy via Relative Information Encoding in 3D Human Pose Estimation. Proceedings of the 29th ACM International Conference on Multimedia, Chengdu, China.","DOI":"10.1145\/3474085.3475504"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 18\u201323). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2019.00584"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/2011\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:32:00Z","timestamp":1760135520000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/2011"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,4]]},"references-count":44,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22052011"],"URL":"https:\/\/doi.org\/10.3390\/s22052011","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,4]]}}}