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Sensors 2015, 15(4), 7708-7727; doi:10.3390/s150407708

A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors

1
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
2
University of Chinese Academy of Sciences, Shanghai 201210, China
*
Author to whom correspondence should be addressed.
Academic Editors: Kourosh Khoshelham and Sisi Zlatanova
Received: 9 December 2014 / Revised: 2 March 2015 / Accepted: 19 March 2015 / Published: 30 March 2015
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)
View Full-Text   |   Download PDF [1063 KB, uploaded 30 March 2015]   |  

Abstract

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. View Full-Text
Keywords: inertial sensors; personal inertial navigation system; zero velocity detector; bayesian network; kinesiology inertial sensors; personal inertial navigation system; zero velocity detector; bayesian network; kinesiology
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xu, Z.; Wei, J.; Zhang, B.; Yang, W. A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors. Sensors 2015, 15, 7708-7727.

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