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Related Experiment Video

Updated: May 26, 2026

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

A zero velocity detection algorithm using inertial sensors for pedestrian navigation systems.

Sang Kyeong Park¹, Young Soo Suh

¹Department of Electrical Engineering, University of Ulsan, Namgu, Ulsan 680-749, Korea. damiro76@hotmail.com

Sensors (Basel, Switzerland)

|December 14, 2011

Summary

This summary is machine-generated.

Reliable pedestrian navigation relies on accurate zero velocity detection. This study introduces a new algorithm using a single gyroscope and a Markov model for improved zero velocity interval detection in inertial navigation systems.

Keywords:

Kalman filter hidden Markov model pedestrian navigation system zero velocity update method

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Last Updated: May 26, 2026

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

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Published on: April 13, 2016

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

Area of Science:

* Pedestrian Navigation Systems
* Inertial Navigation Algorithms
* Sensor Data Processing

Background:

* Pedestrian navigation systems compute position using inertial navigation algorithms.
* Zero velocity updating is crucial for these algorithms, requiring reliable detection of zero velocity intervals to reset velocity errors.
* Current methods for zero velocity detection can be unreliable, impacting navigation accuracy.

Purpose of the Study:

* To propose a novel and more reliable zero velocity detection algorithm for pedestrian navigation systems.
* To enhance the accuracy of inertial navigation by improving the detection of zero velocity intervals.
* To reduce reliance on multiple sensor inputs by utilizing a single gyroscope value.

Main Methods:

* Development of a new zero velocity detection algorithm.
* Utilization of a single gyroscope value as input.
* Construction of a Markov model based on segmented gyroscope outputs, rather than direct gyroscope readings.

Main Results:

* The proposed algorithm demonstrates increased reliability in detecting zero velocity intervals.
* Using a Markov model derived from segmented gyroscope data improves detection accuracy compared to direct sensor output analysis.
* The method successfully integrates into inertial navigation algorithms for pedestrian positioning.

Conclusions:

* The novel zero velocity detection algorithm offers a more robust solution for pedestrian navigation.
* Employing a Markov model with segmented gyroscope data enhances the reliability of zero velocity updating.
* This approach contributes to more accurate and dependable pedestrian positioning systems.