Next Article in Journal
Context Sensing System Analysis for Privacy Preservation Based on Game Theory
Next Article in Special Issue
Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction
Previous Article in Journal
Synthesizing Existing CSMA and TDMA Based MAC Protocols for VANETs
Previous Article in Special Issue
A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(2), 340; doi:10.3390/s17020340

Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking

School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315200, China
Institute for Infocomm Research, Singapore 138632, Singapore
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
School of Electronic and Electrical Engineering, School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 21 November 2016 / Revised: 5 February 2017 / Accepted: 7 February 2017 / Published: 10 February 2017
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
View Full-Text   |   Download PDF [1869 KB, uploaded 10 February 2017]   |  


The wearable inertial/magnetic sensor based human motion analysis plays an important role in many biomedical applications, such as physical therapy, gait analysis and rehabilitation. One of the main challenges for the lower body bio-motion analysis is how to reliably provide position estimations of human subject during walking. In this paper, we propose a particle filter based human position estimation method using a foot-mounted inertial and magnetic sensor module, which not only uses the traditional zero velocity update (ZUPT), but also applies map information to further correct the acceleration double integration drift and thus improve estimation accuracy. In the proposed method, a simple stance phase detector is designed to identify the stance phase of a gait cycle based on gyroscope measurements. For the non-stance phase during a gait cycle, an acceleration control variable derived from ZUPT information is introduced in the process model, while vector map information is taken as binary pseudo-measurements to further enhance position estimation accuracy and reduce uncertainty of walking trajectories. A particle filter is then designed to fuse ZUPT information and binary pseudo-measurements together. The proposed human position estimation method has been evaluated with closed-loop walking experiments in indoor and outdoor environments. Results of comparison study have illustrated the effectiveness of the proposed method for application scenarios with useful map information. View Full-Text
Keywords: sensor fusion; gait analysis; bio-motion analysis; rehabilitation; zero velocity update; map information; particle filter sensor fusion; gait analysis; bio-motion analysis; rehabilitation; zero velocity update; map information; particle filter

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Bao, S.-D.; Meng, X.-L.; Xiao, W.; Zhang, Z.-Q. Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking. Sensors 2017, 17, 340.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top