{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T14:58:34Z","timestamp":1789052314575,"version":"build-2803163510"},"reference-count":114,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003327","name":"Department of Industry, Science, and Resources, The Commonwealth of Australia","doi-asserted-by":"publisher","award":["CRCPXI000116"],"award-info":[{"award-number":["CRCPXI000116"]}],"id":[{"id":"10.13039\/501100003327","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable sensors are used in gait analysis to obtain spatiotemporal parameters, with gait events serving as critical markers for foot and lower limb movement. Summarizing detection methods is essential, as accurately identifying gait events and phases are key to deriving precise spatiotemporal parameters through wearable technology. However, a clear understanding of how these sensors, particularly angular velocity and acceleration signals within inertial measurement units, individually or collectively, contribute to the detection of gait events and gait phases is lacking. This review aims to summarize the current state of knowledge on the application for both gyroscopes, with particular emphasis on the role of angular velocity signals, and inertial measurement units with both angular velocity and acceleration signals in identifying gait events, gait phases, and calculating gait spatiotemporal parameters. Gyroscopes remain the primary tool for gait events detection, while inertia measurement units enhance reliability and enable spatiotemporal parameter estimation. Rule-based methods are suitable for controlled environments, whereas machine learning offers flexibility to analyze complex gait conditions. In addition, there is a lack of consensus on optimal sensor configurations for clinical applications. Future research should focus on standardizing sensor configurations and developing robust, adaptable detection methodologies suitable for different gait conditions.<\/jats:p>","DOI":"10.3390\/s25113481","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T08:34:13Z","timestamp":1748853253000},"page":"3481","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Review of Gait Analysis Using Gyroscopes and Inertial Measurement Units"],"prefix":"10.3390","volume":"25","author":[{"given":"Sheng","family":"Lin","sequence":"first","affiliation":[{"name":"School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kerrie","family":"Evans","sequence":"additional","affiliation":[{"name":"School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, Australia"},{"name":"Healthia Limited, Brisbane, QLD 4006, Australia"},{"name":"Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2050, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2405-2127","authenticated-orcid":false,"given":"Dean","family":"Hartley","sequence":"additional","affiliation":[{"name":"School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, Australia"},{"name":"Healthia Limited, Brisbane, QLD 4006, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott","family":"Morrison","sequence":"additional","affiliation":[{"name":"School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, Australia"},{"name":"iOrthotics Pty Ltd., Brisbane, QLD 4030, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7039-8921","authenticated-orcid":false,"given":"Stuart","family":"McDonald","sequence":"additional","affiliation":[{"name":"School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7302-9838","authenticated-orcid":false,"given":"Martin","family":"Veidt","sequence":"additional","affiliation":[{"name":"School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, Australia"},{"name":"Nantong Institute of Advanced Technology, Shanghai Jiaotong University, Nantong 226300, China"},{"name":"Institute of Rehabilitation Medicine and Health Care, Henan Academy of Innovations in Medical Science, Zhengzhou 450000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/S0166-4115(08)60740-2","article-title":"Adaptability of the CNS in Human Walking","volume":"Volume 78","author":"Patla","year":"1991","journal-title":"Advances in Psychology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s12984-018-0458-8","article-title":"Mobility related physical and functional losses due to aging and disease\u2013A motivation for lower limb exoskeletons","volume":"16","author":"Grimmer","year":"2019","journal-title":"J. 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