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A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors.
- Source :
- Sensors (14248220); 2015, Vol. 15 Issue 4, p7708-7727, 20p
- Publication Year :
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- DETECTORS
GAIT in humans
BAYESIAN analysis
KINESIOLOGY
KINEMATICS
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 15
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 102279687
- Full Text :
- https://doi.org/10.3390/s150407708