{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T09:58:15Z","timestamp":1775210295517,"version":"3.50.1"},"reference-count":93,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2014,2,19]],"date-time":"2014-02-19T00:00:00Z","timestamp":1392768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This article presents a review of the methods used in recognition and analysis of the human gait from three different approaches: image processing, floor sensors and sensors placed on the body. Progress in new technologies has led the development of a series of devices and techniques which allow for objective evaluation, making measurements more efficient and effective and providing specialists with reliable information. Firstly, an introduction of the key gait parameters and semi-subjective methods is presented. Secondly, technologies and studies on the different objective methods are reviewed. Finally, based on the latest research, the characteristics of each method are discussed. 40% of the reviewed articles published in late 2012 and 2013 were related to non-wearable systems, 37.5% presented inertial sensor-based systems, and the remaining 22.5% corresponded to other wearable systems. An increasing number of research works demonstrate that various parameters such as precision, conformability, usability or transportability have indicated that the portable systems based on body sensors are promising methods for gait analysis.<\/jats:p>","DOI":"10.3390\/s140203362","type":"journal-article","created":{"date-parts":[[2014,2,19]],"date-time":"2014-02-19T11:10:21Z","timestamp":1392808221000},"page":"3362-3394","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":954,"title":["Gait Analysis Methods: An Overview of Wearable and  Non-Wearable Systems, Highlighting Clinical Applications"],"prefix":"10.3390","volume":"14","author":[{"given":"Alvaro","family":"Muro-de-la-Herran","sequence":"first","affiliation":[{"name":"DeustoTech-Life Unit, DeustoTech Institute of Technology, University of Deusto, Bilbao 48007, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9356-1186","authenticated-orcid":false,"given":"Begonya","family":"Garcia-Zapirain","sequence":"additional","affiliation":[{"name":"DeustoTech-Life Unit, DeustoTech Institute of Technology, University of Deusto, Bilbao 48007, Spain"}]},{"given":"Amaia","family":"Mendez-Zorrilla","sequence":"additional","affiliation":[{"name":"DeustoTech-Life Unit, DeustoTech Institute of Technology, University of Deusto, Bilbao 48007, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2014,2,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1007\/978-3-540-69139-6_178","article-title":"Emerging Trends of Body-Mounted Sensors in Sports and Human Gait Analysis","volume":"Volume 21","author":"Osman","year":"2008","journal-title":"4th Kuala Lumpur International Conference on Biomedical Engineering 2008"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1177\/0363546513482718","article-title":"Gait patterns differ between ACL-reconstructed athletes who pass return-to-sport criteria and those who fail","volume":"41","author":"Logerstedt","year":"2013","journal-title":"Am. 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