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Sensor-based alternatives often trade accuracy for flexibility, making validation essential because residual errors can originate from environmental, methodological, or processing sources. This work improves validation by applying machine learning to study how such errors affect gait profile assessment. Two datasets from healthy and post-stroke participants are used to compare a motion capture system with a 3D camera-based system. 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The study analyzes spatiotemporal, asymmetry, and body center of mass parameters to characterize normal and pathologic gait features.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},291002,1789643164,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},13056703019662,"https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nMachine-Learning-Based Validation of Microsoft Azure Kinect in Measuring Gait Profiles  \nOriginal  \nMachine-Learning-Based Validation of Microsoft Azure Kinect in Measuring Gait Profiles / Ferraris, Claudia; Amprimo, Gianluca; Cerfoglio, Serena; Masi, Giulia; Vismara, Luca; Cimolin, Veronica. -In: ELECTRONICS. -ISSN 2079-9292. - 13:23(2024) . [10 .3390/electronics13234739]  \nAvailability:  \nThis version is available at: 11583/3002439 since: 2025-08-17T11:06:25Z  \nPublisher:  \nMultidisciplinary Digital Publishing Institute (MDPI)  \nPublished  \nDOI:10.3390/electronics13234739  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n21 February 2026  \nArticle  \nMachine-Learning-Based Validation of Microsoft Azure Kinect in Measuring Gait Profiles  \nClaudia Ferraris 1, *,†, Gianluca Amprimo 1,2,†, Serena Cerfoglio 3,4, Giulia Masi 2, Luca Vismara 4 and Veronica Cimolin 3,4  \nCitation: Ferraris, C.; Amprimo, G.; Cerfoglio, S.; Masi, G.; Vismara, L.; Cimolin, V. Machine-Learning-Based Validation of Microsoft Azure Kinect in Measuring Gait Profiles. Electronics 2024, 13, 4739. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/electronics13234739](10.3390/electronics13234739)  \nAcademic Editor: Michael Winter  \nReceived: 6 November 2024  \nRevised: 28 November 2024  \nAccepted: 28 November 2024  \nPublished: 29 November 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), National Research Council (CNR), 10129 Turin, Italy; gianluca.amprimo@polito.it  \n2 Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy;  \ngiulia.masi@polito.it  \n3 Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; [serena.cerfoglio@polimi.it](serena.cerfoglio@polimi.it) (S.C.); [veronica.cimolin@polimi.it](veronica.cimolin@polimi.it) (V.C.)  \n4 IRCCS Istituto Auxologico Italiano, Division of Neurology and Neurorehabilitation, 28824 Piancavallo, Italy; [lucavisma@hotmail.com](lucavisma@hotmail.com)  \n* Correspondence: [claudia.ferraris@cnr.it](claudia.ferraris@cnr.it); Tel.: +39-011-0905404 † These authors contributed equally to this work.  \nAbstract: Gait is one of the most extensively studied motor tasks using motion capture systems, the gold standard for instrumental gait analysis. Various sensor-based solutions have been recently proposed to evaluate gait parameters, typically providing lower accuracy but greater flexibility. Validation procedures are crucial to assess the measurement accuracy of these solutions since residual errors may arise from environmental, methodological, or processing factors. This study aims to enhance validation by employing machine learning techniques to investigate the impact of such errors on the overall assessment of gait profiles. Two datasets of gait trials, collected from healthy and post-stroke subjects using a motion capture system and a 3D camera-based system, were considered. The estimated gait profiles include spatiotemporal, asymmetry, and body center of mass parameters to capture various normal and pathologic gait peculiarities. Machine learning models show the equivalence and the high level of agreement and concordance between the measurement systems in assessing gait profiles (accuracy: 98.7%) . In addition, they demonstrate data interchangeability and integrability despite residual errors identified by traditional statistical metric","cbCaib6RzXHkDk2f","https://ap.wps.com/l/cbCaib6RzXHkDk2f","pdf",1504608,21,"English","# Introduction\n## Validation of sensor-based gait measurement\n## Machine learning approach for gait profile assessment\n# Datasets and measurement systems\n## Healthy and post-stroke cohorts\n## Spatiotemporal and body center of mass parameters\n# Results and findings\n## Agreement and concordance between systems\n## Interchangeability despite residual errors","[{\"question\":\"Why are validation procedures important for sensor-based gait measurement systems?\",\"answer\":\"Validation is crucial to assess measurement accuracy because residual errors can arise from environmental, methodological, or processing factors, potentially affecting the reliability of gait profile estimates.\"},{\"question\":\"How does this study use machine learning in gait profile validation?\",\"answer\":\"Machine learning models are used to investigate the impact of residual errors on gait profile assessment and to compare measurement systems based on the estimated gait parameters.\"},{\"question\":\"What datasets and parameters are used to compare the two measurement systems?\",\"answer\":\"Two datasets of gait trials are collected from healthy and post-stroke subjects using both a motion capture system and a 3D camera-based system. The study analyzes spatiotemporal, asymmetry, and body center of mass parameters to characterize normal and pathologic gait features.\"}]","Machine-Learning-Based Validation of Microsoft Azure Kinect in Measuring Gait Profiles - Article | PDF",53]