{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:29:20Z","timestamp":1785421760077,"version":"3.56.0"},"reference-count":42,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/019w4f821","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Assessment of upper-limb kinematics is essential in clinical practice for diagnosis, rehabilitation monitoring, and treatment personalization. Markerless Motion Capture (MMC) systems, such as the Microsoft Azure Kinect (AK), offer a low-cost and time-efficient alternative to marker-based systems. However, while AK accuracy has been extensively studied for lower-limb movements, its performance for upper-limb analysis\u2014especially under clinically relevant, suboptimal conditions\u2014remains underexplored. This study aims to validate AK for upper-limb motion tracking against a gold-standard optoelectronic system under optimal and suboptimal conditions. Sixteen healthy adults performed ten upper body motor tasks in three scenarios: optimal setup, seated posture with table occlusion, and increased camera distance. Joint angles were compared using normalized Root Mean Squared Error (nRMSE) and Pearson\u2019s correlation coefficient. Performance Indicators (PIs) including Range of Motion (ROM), smoothness, and Time to Peak Velocity (TTPV) were also evaluated. AK accurately captured movements performed within the camera plane, with median nRMSE below 20% in optimal conditions and no significant degradation in suboptimal setups. In contrast, movements occurring on planes perpendicular to the camera were poorly captured. ROM estimation was acceptable and highly reproducible, while TTPV showed moderate-to-poor reliability and smoothness deviated substantially from the reference system. These findings suggest that careful attention to Kinect positioning is essential to ensure effective acquisitions, even in suboptimal scenarios. Future research should evaluate AK validity in clinical populations and explore the effects of system interference in multi-device setups.<\/jats:p>","DOI":"10.3390\/s26134098","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T08:45:20Z","timestamp":1782722720000},"page":"4098","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Validation of Azure Kinect for Upper Limb Motion Analysis Under Optimal and Suboptimal Conditions"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6916-3432","authenticated-orcid":false,"given":"Gabriele","family":"Fassina","sequence":"first","affiliation":[{"name":"NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1983-8204","authenticated-orcid":false,"given":"Serena","family":"Cerfoglio","sequence":"additional","affiliation":[{"name":"Movlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy"},{"name":"IRCCS Istituto Auxologico Italiano, San Giuseppe Hospital, Piancavallo, 28823 Verbania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Rigucci","sequence":"additional","affiliation":[{"name":"NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9957-2786","authenticated-orcid":false,"given":"Alessandra","family":"Pedrocchi","sequence":"additional","affiliation":[{"name":"NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6299-7254","authenticated-orcid":false,"given":"Veronica","family":"Cimolin","sequence":"additional","affiliation":[{"name":"Movlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy"},{"name":"IRCCS Istituto Auxologico Italiano, San Giuseppe Hospital, Piancavallo, 28823 Verbania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6527-0779","authenticated-orcid":false,"given":"Emilia","family":"Ambrosini","sequence":"additional","affiliation":[{"name":"NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106324","DOI":"10.1016\/j.cmpb.2021.106324","article-title":"Postural control assessment via Microsoft Azure Kinect DK: An evaluation study","volume":"209","author":"Antico","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Romeo, L., Marani, R., Malosio, M., Perri, A.G., and D\u2019Orazio, T. (2021, January 22\u201325). Performance analysis of body tracking with the microsoft azure kinect. Proceedings of the 2021 29th Mediterranean Conference on Control and Automation, MED, Puglia, Italy.","DOI":"10.1109\/MED51440.2021.9480177"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e12995","DOI":"10.7717\/peerj.12995","article-title":"Applications and limitations of current markerless motion capture methods for clinical gait biomechanics","volume":"10","author":"Wade","year":"2022","journal-title":"PeerJ"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.gaitpost.2018.11.029","article-title":"Three-dimensional cameras and skeleton pose tracking for physical function assessment: A review of uses, validity, current developments and Kinect alternatives","volume":"68","author":"Clark","year":"2019","journal-title":"Gait Posture"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1177\/1747493017711813","article-title":"Standardized measurement of sensorimotor recovery in stroke trials: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable","volume":"12","author":"Kwakkel","year":"2017","journal-title":"Int. J. Stroke"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e21606","DOI":"10.1016\/j.heliyon.2023.e21606","article-title":"Azure Kinect performance evaluation for human motion and upper limb biomechanical analysis","volume":"9","author":"Brambilla","year":"2023","journal-title":"Heliyon"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Albert, J.A., Owolabi, V., Gebel, A., Brahms, C.M., Granacher, U., and Arnrich, B. (2020). Evaluation of the pose tracking performance of the azure kinect and kinect v2 for gait analysis in comparison with a gold standard: A pilot study. Sensors, 20.","DOI":"10.3390\/s20185104"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ferraris, C., Amprimo, G., Cerfoglio, S., Masi, G., Vismara, L., and Cimolin, V. (2024). Machine-Learning-Based Validation of Microsoft Azure Kinect in Measuring Gait Profiles. Electronics, 13.","DOI":"10.3390\/electronics13234739"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.jclinepi.2013.08.015","article-title":"Systematic review of the Hawthorne effect: New concepts are needed to study research participation effects","volume":"67","author":"McCambridge","year":"2014","journal-title":"J. Clin. Epidemiol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Armitano-Lago, C., Willoughby, D., and Kiefer, A.W. (2022). A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport. Front. Sport. Act. Living, 3.","DOI":"10.3389\/fspor.2021.809898"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s12984-023-01186-9","article-title":"A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation","volume":"20","author":"Lam","year":"2023","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cotton, R.J., Cimorelli, A., Shah, K., Anarwala, S., Uhlrich, S., and Karakostas, T. (2023, January 24\u201327). Improved Trajectory Reconstruction for Markerless Pose Estimation. Proceedings of the 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia.","DOI":"10.1109\/EMBC40787.2023.10340745"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Uhlrich, S.D., Falisse, A., Muccini, J., Ko, M., Chaudhari, A.S., Hicks, J.L., and Delp, S.L. (2023). OpenCap: Human movement dynamics from smartphone videos. PLoS Comput. Biol., 19.","DOI":"10.1371\/journal.pcbi.1011462"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"24","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\u2014Open"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ferraris, C., Amprimo, G., Masi, G., Vismara, L., Cremascoli, R., Sinagra, S., Pettiti, G., Mauro, A., and Priano, L. (2022). Evaluation of Arm Swing Features and Asymmetry during Gait in Parkinson\u2019s Disease Using the Azure Kinect Sensor. Sensors, 22.","DOI":"10.3390\/s22166282"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.1007\/s10072-023-07296-5","article-title":"Kinect-based objective assessment of the acute levodopa challenge test in parkinsonism: A feasibility study","volume":"45","author":"Hong","year":"2024","journal-title":"Neurol. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cerfoglio, S., Ferraris, C., Vismara, L., Amprimo, G., Priano, L., Bigoni, M., Galli, M., Mauro, A., and Cimolin, V. (2024). Estimation of gait parameters in healthy and hemiplegic individuals using Azure Kinect: A comparative study with the optoelectronic system. Front. Bioeng. Biotechnol., 12.","DOI":"10.3389\/fbioe.2024.1449680"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1007\/s12369-020-00704-2","article-title":"A Close Look at the Imitation Performance of Children with Autism and Typically Developing Children Using a Robotic System","volume":"13","author":"Taheri","year":"2020","journal-title":"Int. J. Soc. Robot."},{"key":"ref_19","unstructured":"Pham, H.H., Khoudour, L., Crouzil, A., Zegers, P., and Velastin, S.A. (2018). Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, P.M., Kwong, P.W., Lai, C.K., and Ng, S.S. (2019). Comparison of bilateral and unilateral upper limb training in people with stroke: A systematic review and meta-analysis. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0216357"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Koontz, A.M., Neti, A., Chung, C.S., Ayiluri, N., Slavens, B.A., Davis, C.G., and Wei, L. (2022). Reliability of 3D Depth Motion Sensors for Capturing Upper Body Motions and Assessing the Quality of Wheelchair Transfers. Sensors, 22.","DOI":"10.3390\/s22134977"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fassina, G., Santos, L., Zorzella, E., Caglio, A., Annunziata, S., Cavallini, A., Pedrocchi, A., and Ambrosini, E. (2024, January 26\u201330). Song Gesture Recognition for a Robot-Enhanced Imitation Therapy. Proceedings of the 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN), Pasadena, CA, USA.","DOI":"10.1109\/RO-MAN60168.2024.10731276"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bertram, J., Kr\u00fcger, T., R\u00f6hling, H.M., Jelusic, A., Mansow-Model, S., Schniepp, R., Wuehr, M., and Otte, K. (2023). Accuracy and repeatability of the Microsoft Azure Kinect for clinical measurement of motor function. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0279697"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.gaitpost.2021.04.005","article-title":"Effects of camera viewing angles on tracking kinematic gait patterns using Azure Kinect, Kinect v2 and Orbbec Astra Pro v2","volume":"87","author":"Yeung","year":"2021","journal-title":"Gait Posture"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.gaitpost.2022.05.021","article-title":"Comparison of Azure Kinect overground gait spatiotemporal parameters to marker based optical motion capture","volume":"96","author":"Guess","year":"2022","journal-title":"Gait Posture"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yoshimoto, K., and Shinya, M. (2022). Use of the Azure Kinect to measure foot clearance during obstacle crossing: A validation study. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0265215"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.gaitpost.2022.03.011","article-title":"Comparison of Azure Kinect and optical retroreflective motion capture for kinematic and spatiotemporal evaluation of the sit-to-stand test","volume":"94","author":"Thomas","year":"2022","journal-title":"Gait Posture"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jo, S., Song, S., Kim, J., and Song, C. (2022). Agreement between Azure Kinect and Marker-Based Motion Analysis during Functional Movements: A Feasibility Study. Sensors, 22.","DOI":"10.3390\/s22249819"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7175240","DOI":"10.1155\/2019\/7175240","article-title":"Validity and Reliability of Upper Limb Functional Assessment Using the Microsoft Kinect V2 Sensor","volume":"2019","author":"Cai","year":"2019","journal-title":"Appl. Bionics Biomech."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cai, L., Liu, D., and Ma, Y. (2021). Placement Recommendations for Single Kinect-Based Motion Capture System in Unilateral Dynamic Motion Analysis. Healthcare, 9.","DOI":"10.3390\/healthcare9081076"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Scano, A., Mira, R.M., Cerveri, P., Tosatti, L.M., and Sacco, M. (2020). Analysis of Upper-Limb and Trunk Kinematic Variability: Accuracy and Reliability of an RGB-D Sensor. Multimodal Technol. Interact., 4.","DOI":"10.3390\/mti4020014"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Faity, G., Mottet, D., and Froger, J. (2022). Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching. Sensors, 22.","DOI":"10.1101\/2022.01.18.476737"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1016\/j.jse.2022.04.007","article-title":"Reliability and agreement of Azure Kinect and Kinect v2 depth sensors in the shoulder joint range of motion estimation","volume":"31","year":"2022","journal-title":"J. Shoulder Elb. Surg."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4333","DOI":"10.17973\/MMSJ.2021_6_2021012","article-title":"Azure kinect body tracking under review for the specific case of upper limb exercises","volume":"2021","author":"Ivorra","year":"2021","journal-title":"MM Sci. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1111\/j.1468-1331.2008.02396.x","article-title":"Quantitative evaluation of functional limitation of upper limb movements in subjects affected by ataxia","volume":"16","author":"Menegoni","year":"2009","journal-title":"Eur. J. Neurol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s12984-022-01082-8","article-title":"A unified scheme for the benchmarking of upper limb functions in neurological disorders","volume":"19","author":"Longatelli","year":"2022","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0966-6362(01)00155-2","article-title":"A method for determination of upper extremity kinematics","volume":"15","author":"Rab","year":"2002","journal-title":"Gait Posture"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s12984-015-0090-9","article-title":"On the analysis of movement smoothness","volume":"12","author":"Balasubramanian","year":"2015","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_39","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_40","unstructured":"Unger, T., Moslehian, A.S., Peiffer, J.D., Ullrich, J., Gassert, R., Lambercy, O., Cotton, R.J., and Easthope, C.A. (2024). Differentiable Biomechanics for Markerless Motion Capture in Upper Limb Stroke Rehabilitation: A Comparison with Optical Motion Capture. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1109\/TNSRE.2023.3251440","article-title":"Concurrent Validity of a Custom Method for Markerless 3D Full-Body Motion Tracking of Children and Young Adults based on a Single RGB-D Camera","volume":"31","author":"Hesse","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s10055-025-01124-1","article-title":"Augmented reality for upper limb rehabilitation: Real-time kinematic feedback with HoloLens 2","volume":"29","author":"Luciani","year":"2025","journal-title":"Virtual Real."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/4098\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T04:41:53Z","timestamp":1782967313000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/4098"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,27]]},"references-count":42,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["s26134098"],"URL":"https:\/\/doi.org\/10.3390\/s26134098","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,27]]}}}