{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T19:51:55Z","timestamp":1785700315571,"version":"3.56.0"},"reference-count":182,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2012,2,16]],"date-time":"2012-02-16T00:00:00Z","timestamp":1329350400000},"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>Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications.<\/jats:p>","DOI":"10.3390\/s120202255","type":"journal-article","created":{"date-parts":[[2012,2,16]],"date-time":"2012-02-16T11:11:43Z","timestamp":1329390703000},"page":"2255-2283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":993,"title":["Gait Analysis Using Wearable Sensors"],"prefix":"10.3390","volume":"12","author":[{"given":"Weijun","family":"Tao","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology, 185 MIyanokuchi, Tosayamada-Cho, Kami-City, Kochi 782-8502, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rencheng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Nakano Lab, Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hutian","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2012,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.gaitpost.2003.10.001","article-title":"Assessment and validation of a simple automated method for the detection of gait events and intervals","volume":"20","author":"Ghoussayni","year":"2004","journal-title":"Gait Posture"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MSP.2005.1550191","article-title":"Gait recognition: A challenging signal processing technology for biometric identification","volume":"22","author":"Boulgouris","year":"2005","journal-title":"IEEE Signal Process. 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