{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T20:51:41Z","timestamp":1760820701155,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2015,3,30]],"date-time":"2015-03-30T00:00:00Z","timestamp":1427673600000},"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>This paper proposes a robust zero velocity (ZV) detector algorithm to accurately calculate stationary periods in a gait cycle. The proposed algorithm adopts an effective gait cycle segmentation method and introduces a Bayesian network (BN) model based on the measurements of inertial sensors and kinesiology knowledge to infer the ZV period. During the detected ZV period, an Extended Kalman Filter (EKF) is used to estimate the error states and calibrate the position error. The experiments reveal that the removal rate of ZV false detections by the proposed method increases 80% compared with traditional method at high walking speed. Furthermore, based on the detected ZV, the Personal Inertial Navigation System (PINS) algorithm aided by EKF performs better, especially in the altitude aspect.<\/jats:p>","DOI":"10.3390\/s150407708","type":"journal-article","created":{"date-parts":[[2015,3,30]],"date-time":"2015-03-30T10:50:23Z","timestamp":1427712623000},"page":"7708-7727","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Robust Method to Detect Zero Velocity for Improved  3D Personal Navigation Using Inertial Sensors"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhengyi","family":"Xu","sequence":"first","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"University of Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianming","family":"Wei","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4303","DOI":"10.3390\/s130404303","article-title":"Use of high sensitivity gnss receiver doppler measurements for indoor pedestrian dead reckoning","volume":"13","author":"He","year":"2013","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7606","DOI":"10.3390\/s110807606","article-title":"Indoor pedestrian navigation using foot-mounted imu and portable ultrasound range sensors","volume":"11","author":"Girard","year":"2011","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3768","DOI":"10.3390\/s140203768","article-title":"Implementation and performance of a GPS\/INS tightly coupled assisted pll architecture using MEMS inertial sensors","volume":"14","author":"Tawk","year":"2014","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Faramondi, L., Inderst, F., Pascucci, F., Setola, R., and Delprato, U. 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