{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T07:09:45Z","timestamp":1769929785572,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"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>Gait analysis may serve various purposes related to health care, such as the estimation of elderly people\u2019s risk of falling. This paper is devoted to gait analysis based on data from depth sensors which are suitable for use both at healthcare facilities and in monitoring systems dedicated to household environments. This paper is focused on the comparison of three methods for spatiotemporal gait analysis based on data from depth sensors, involving the analysis of the movement trajectories of the knees, feet, and centre of mass. The accuracy of the results obtained using those methods was assessed for different depth sensors\u2019 viewing angles and different types of subject clothing. Data were collected using a Kinect v2 device. Five people took part in the experiments. Data from a Zebris FDM platform were used as a reference. The obtained results indicate that the viewing angle and the subject\u2019s clothing affect the uncertainty of the estimates of spatiotemporal gait parameters, and that the method based on the trajectories of the feet yields the most information, while the method based on the trajectory of the centre of mass is the most robust.<\/jats:p>","DOI":"10.3390\/s23031218","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T01:36:26Z","timestamp":1674437786000},"page":"1218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2739-4578","authenticated-orcid":false,"given":"Jakub","family":"Wagner","sequence":"first","affiliation":[{"name":"Institute of Radioelectronics and Multimedia Technology, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}]},{"given":"Marcin","family":"Szyma\u0144ski","sequence":"additional","affiliation":[{"name":"Institute of Radioelectronics and Multimedia Technology, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8452-6824","authenticated-orcid":false,"given":"Michalina","family":"B\u0142a\u017ckiewicz","sequence":"additional","affiliation":[{"name":"Chair of Physiotherapy Fundamentals, Faculty of Rehabilitation, J\u00f3zef Pi\u0142sudski University of Physical Education in Warsaw, Marymoncka 34, 00-968 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4110-8571","authenticated-orcid":false,"given":"Katarzyna","family":"Kaczmarczyk","sequence":"additional","affiliation":[{"name":"Chair of Physiotherapy Fundamentals, Faculty of Rehabilitation, J\u00f3zef Pi\u0142sudski University of Physical Education in Warsaw, Marymoncka 34, 00-968 Warsaw, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Montero-Odasso, M., and Camicioli, R. 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