{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:14:23Z","timestamp":1777655663218,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,17]],"date-time":"2020-05-17T00:00:00Z","timestamp":1589673600000},"content-version":"vor","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>This work proposes to improve the accuracy of joint angle estimates obtained from an RGB-D sensor. It is based on a constrained extended Kalman Filter that tracks inputted measured joint centers. Since the proposed approach uses a biomechanical model, it allows physically consistent constrained joint angles and constant segment lengths to be obtained. A practical method that is not sensor-specific for the optimal tuning of the extended Kalman filter covariance matrices is provided. It uses reference data obtained from a stereophotogrammetric system but it has to be tuned only once since it is task-specific only. The improvement of the optimal tuning over classical methods in setting the covariance matrices is shown with a statistical parametric mapping analysis. The proposed approach was tested with six healthy subjects who performed four rehabilitation tasks. The accuracy of joint angle estimates was assessed with a reference stereophotogrammetric system. Even if some joint angles, such as the internal\/external rotations, were not well estimated, the proposed optimized algorithm reached a satisfactory average root mean square difference of 9.7     \u2218     and a correlation coefficient of 0.8 for all joints. Our results show that an affordable RGB-D sensor can be used for simple in-home rehabilitation when using a constrained biomechanical model.<\/jats:p>","DOI":"10.3390\/s20102848","type":"journal-article","created":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T02:43:42Z","timestamp":1589769822000},"page":"2848","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Physically Consistent Whole-Body Kinematics Assessment Based on an RGB-D Sensor. Application to Simple Rehabilitation Exercises"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4355-8018","authenticated-orcid":false,"given":"Jessica","family":"Colombel","sequence":"first","affiliation":[{"name":"Universit\u00e9 de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent","family":"Bonnet","sequence":"additional","affiliation":[{"name":"LISSI, Univ. Paris Est Creteil, F-94400 Vitry, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Daney","sequence":"additional","affiliation":[{"name":"Inria Bordeaux Sud Ouest - IMS (UMR 5218), F-33405 Talence, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0368-8248","authenticated-orcid":false,"given":"Raphael","family":"Dumas","sequence":"additional","affiliation":[{"name":"Univ. Lyon, Univ. Gustave Eiffel, LBMC (UMR T9406), F-69675 Lyon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antoine","family":"Seilles","sequence":"additional","affiliation":[{"name":"NaturalPad, 34090 Montpellier, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fran\u00e7ois","family":"Charpillet","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Brunnekreef, J.J., van Uden, C.J., van Moorsel, S., and Kooloos, J.G. (2005). Reliability of videotaped observational gait analysis in patients with orthopedic impairments. BMC Musculoskelet. Disord., 6.","DOI":"10.1186\/1471-2474-6-17"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.arth.2008.05.019","article-title":"Accuracy of Knee Range of Motion Assessment after Total Knee Arthroplasty","volume":"23","author":"Lavernia","year":"2008","journal-title":"J. Arthroplast."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"030801","DOI":"10.1115\/1.4038741","article-title":"Multibody Kinematics Optimization for the Estimation of Upper and Lower Limb Human Joint Kinematics: A Systematized Methodological Review","volume":"140","author":"Begon","year":"2018","journal-title":"J. Biomech. Eng."},{"key":"ref_4","first-page":"277","article-title":"The use of commercial video games in rehabilitation: A systematic review","volume":"36","author":"Jansen","year":"2016","journal-title":"Int. J. Rehabil. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7821","DOI":"10.1109\/JSEN.2016.2609392","article-title":"Wearable Inertial Sensors for Human Motion Analysis: A Review","volume":"16","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1016\/j.patrec.2013.02.006","article-title":"A survey of human motion analysis using depth imagery","volume":"34","author":"Chen","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., and Sheikh, Y. (2018). OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. arXiv.","DOI":"10.1109\/CVPR.2017.143"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, Z., Sedlar, J., Carpentier, J., Laptev, I., Mansard, N., and Sivic, J. (2019, January 15\u201320). Estimating 3D Motion and Forces of Person-Object Interactions From Monocular Video. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00884"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1089\/g4h.2014.0047","article-title":"Motor Rehabilitation Using Kinect: A Systematic Review","volume":"4","author":"Fallavollita","year":"2015","journal-title":"Games Health J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, Q., Kurillo, G., Ofli, F., and Bajcsy, R. (2015, January 21\u201323). Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect. Proceedings of the International Conference on Healthcare Informatics, Dallas, TX, USA.","DOI":"10.1109\/ICHI.2015.54"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Naeemabadi, M., Dinesen, B., Andersen, O.K., Najafi, S., and Hansen, J. (2020, May 08). Evaluating Accuracy and Usability of Microsoft Kinect Sensors and Wearable Sensor for Tele Knee Rehabilitation after Knee Operation. Available online: https:\/\/www.scitepress.org\/papers\/2018\/65782\/65782.pdf.","DOI":"10.5220\/0006578201280135"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.gaitpost.2019.03.020","article-title":"Reliability and validity of the Kinect V2 for the assessment of lower extremity rehabilitation exercises","volume":"70","author":"Wochatz","year":"2019","journal-title":"Gait Posture"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1016\/j.gaitpost.2014.01.008","article-title":"Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson\u2019s disease","volume":"39","author":"Galna","year":"2014","journal-title":"Gait Posture"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Otte, K., Kayser, B., Mansow-Model, S., Verrel, J., Paul, F., Brandt, A.U., and Schmitz-H\u00fcbsch, T. (2016). Accuracy and Reliability of the Kinect Version 2 for Clinical Measurement of Motor Function. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0166532"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bonnech\u00e8re, B., Sholukha, V., Omelina, L., Van Sint, S., and Jansen, B. (2018). 3D Analysis of Upper Limbs Motion during Rehabilitation Exercises Using the KinectTM Sensor: Development, Laboratory Validation and Clinical Application. Sensors, 18.","DOI":"10.20944\/preprints201805.0435.v1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.gaitpost.2016.04.004","article-title":"Accuracy of KinectOne to quantify kinematics of the upper body","volume":"47","author":"Kuster","year":"2016","journal-title":"Gait Posture"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.cviu.2017.01.011","article-title":"Space-time representation of people based on 3D skeletal data: A review","volume":"158","author":"Han","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4291","DOI":"10.1007\/s11042-016-3546-4","article-title":"Filtered pose graph for efficient kinect pose reconstruction","volume":"76","author":"Plantard","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"181","DOI":"10.4218\/etrij.17.2816.0045","article-title":"Motion Capture of the Human Body Using Multiple Depth Sensors","volume":"39","author":"Kim","year":"2017","journal-title":"ETRI J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s11044-017-9573-8","article-title":"A musculoskeletal model driven by dual Microsoft Kinect Sensor data","volume":"41","author":"Skals","year":"2017","journal-title":"Multibody Syst. Dyn."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4405","DOI":"10.1007\/s11042-015-3177-1","article-title":"A survey of depth and inertial sensor fusion for human action recognition","volume":"76","author":"Chen","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","unstructured":"Feng, S., and Murray-Smith, R. (2014, January 30). Fusing Kinect sensor and inertial sensors with multi-rate Kalman filter. Proceedings of the IET Conference on Data Fusion and Target Tracking: Algorithms and Applications (DF TT 2014), Liverpool, UK."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4317","DOI":"10.1007\/s00542-018-3769-6","article-title":"An IMU-compensated skeletal tracking system using Kinect for the upper limb","volume":"24","author":"Du","year":"2018","journal-title":"Microsyst. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tripathy, S.R., Chakravarty, K., and Sinha, A. (2018, January 3\u20137). Constrained Particle Filter for Improving Kinect Based Measurements. Proceedings of the 26th European Signal Processing Conference (EUSIPCO), Rome, Italy.","DOI":"10.23919\/EUSIPCO.2018.8553437"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10665-014-9689-2","article-title":"Application of extended Kalman filter for improving the accuracy and smoothness of Kinect skeleton-joint estimates","volume":"88","author":"Shu","year":"2014","journal-title":"J. Eng. Math."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1017\/S0263574797000180","article-title":"SYMORO+: A system for the symbolic modelling of robots","volume":"15","author":"Khalil","year":"1997","journal-title":"Robotica"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1016\/j.jbiomech.2004.05.042","article-title":"International Society of Biomechanics, Standardization and Terminology Committee. ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion\u2014Part II: Shoulder, elbow, wrist and hand","volume":"38","author":"Wu","year":"2005","journal-title":"J. Biomech."},{"key":"ref_29","unstructured":"Gupta, N., and Hauser, R. (2017). Kalman Filtering with Equality and Inequality State Constraints. arXiv, Available online: arxiv.org\/abs\/0709.2791."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4275","DOI":"10.1109\/JSEN.2015.2416651","article-title":"Evaluating and Improving the Depth Accuracy of Kinect for Windows v2","volume":"15","author":"Yang","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/JSEN.2018.2876624","article-title":"Influence of a Marker-Based Motion Capture System on the Performance of Microsoft Kinect v2 Skeleton Algorithm","volume":"19","author":"Naeemabadi","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_32","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":"Davis","year":"1991","journal-title":"Hum. Mov. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/BF02344878","article-title":"Real-time human motion estimation using biomechanical models and non-linear state-space filters","volume":"41","author":"Cerveri","year":"2003","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3390","DOI":"10.1016\/j.jbiomech.2008.09.035","article-title":"Kalman smoothing improves the estimation of joint kinematics and kinetics in marker-based human gait analysis","volume":"41","author":"Jonkers","year":"2008","journal-title":"J. Biomech."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1137\/0806023","article-title":"An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds","volume":"6","author":"Coleman","year":"1996","journal-title":"Siam J. Optim."},{"key":"ref_36","unstructured":"Friston, K.J., Ashburner, J.T., Kiebel, S.J., Nichols, T.E., and Penny, W.D. (2011). Statistical Parametric Mapping: The Analysis of Functional Brain Images, Academic Press. [2nd ed.]."},{"key":"ref_37","unstructured":"(2019, July 08). Available online: http:\/\/www.spm1d.org\/."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Donati, M., Camomilla, V., Vannozzi, G., and Cappozzo, A. (2008). Anatomical frame identification and reconstruction for repeatable lower limb joint kinematics estimates. J. Biomech.","DOI":"10.1016\/j.jbiomech.2008.04.018"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2848\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:29:38Z","timestamp":1760174978000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2848"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,17]]},"references-count":38,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20102848"],"URL":"https:\/\/doi.org\/10.3390\/s20102848","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,17]]}}}