Motion monitoring based on a finite state machine for precise indoor localization

Motion monitoring based on a finite state machine for precise indoor localization This paper presents a precise stance detection method for accurate personal localization using a foot-mounted inertial measurement unit. The exact classification of the stance phases of the foot is realized with a finite state machine (FSM), which separates the human gait circle in different sub-states. The FSM-based approach provides high accurate and robust detections of Zero Velocity Updates (ZUPTs) which can be applied to the navigation filter. We use a constraint stochastic cloning (SC) Kalman filter to show the performance of the high precise ZUPT intervals with real world sensor data including forward, backward and staircase motion. Even for the movement type running and the signals of an ultra-low cost inertial measurement unit we achieve with our motion monitoring system a position estimation with an average error of less than 1.5% of the travelled distance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Gyroscopy and Navigation Springer Journals

Motion monitoring based on a finite state machine for precise indoor localization

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Publisher
Pleiades Publishing
Copyright
Copyright © 2017 by Pleiades Publishing, Ltd.
Subject
Engineering; Aerospace Technology and Astronautics; Geophysics/Geodesy
ISSN
2075-1087
eISSN
2075-1109
D.O.I.
10.1134/S2075108717030063
Publisher site
See Article on Publisher Site

Abstract

This paper presents a precise stance detection method for accurate personal localization using a foot-mounted inertial measurement unit. The exact classification of the stance phases of the foot is realized with a finite state machine (FSM), which separates the human gait circle in different sub-states. The FSM-based approach provides high accurate and robust detections of Zero Velocity Updates (ZUPTs) which can be applied to the navigation filter. We use a constraint stochastic cloning (SC) Kalman filter to show the performance of the high precise ZUPT intervals with real world sensor data including forward, backward and staircase motion. Even for the movement type running and the signals of an ultra-low cost inertial measurement unit we achieve with our motion monitoring system a position estimation with an average error of less than 1.5% of the travelled distance.

Journal

Gyroscopy and NavigationSpringer Journals

Published: Aug 23, 2017

References

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