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Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone
This paper presents a pedestrian dead reckoning (PDR) approach based on motion mode recognition using a smartphone. The motion mode consists of pedestrian movement state and phone pose. With the support vector machine (SVM) and the decision tree (DT), the arbitrary combinations of movement state and...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6021937/ https://www.ncbi.nlm.nih.gov/pubmed/29867027 http://dx.doi.org/10.3390/s18061811 |
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author | Wang, Boyuan Liu, Xuelin Yu, Baoguo Jia, Ruicai Gan, Xingli |
author_facet | Wang, Boyuan Liu, Xuelin Yu, Baoguo Jia, Ruicai Gan, Xingli |
author_sort | Wang, Boyuan |
collection | PubMed |
description | This paper presents a pedestrian dead reckoning (PDR) approach based on motion mode recognition using a smartphone. The motion mode consists of pedestrian movement state and phone pose. With the support vector machine (SVM) and the decision tree (DT), the arbitrary combinations of movement state and phone pose can be recognized successfully. In the traditional principal component analysis based (PCA-based) method, the obtained horizontal accelerations in one stride time interval cannot be guaranteed to be horizontal and the pedestrian’s direction vector will be influenced. To solve this problem, we propose a PCA-based method with global accelerations (PCA-GA) to infer pedestrian’s headings. Besides, based on the further analysis of phone poses, an ambiguity elimination method is also developed to calibrate the obtained headings. The results indicate that the recognition accuracy of the combinations of movement states and phone poses can be 92.4%. The 50% and 75% absolute estimation errors of pedestrian’s headings are 5.6° and 9.2°, respectively. This novel PCA-GA based method can achieve higher accuracy than traditional PCA-based method and heading offset method. The localization error can reduce to around 3.5 m in a trajectory of 164 m for different movement states and phone poses. |
format | Online Article Text |
id | pubmed-6021937 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-60219372018-07-02 Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone Wang, Boyuan Liu, Xuelin Yu, Baoguo Jia, Ruicai Gan, Xingli Sensors (Basel) Article This paper presents a pedestrian dead reckoning (PDR) approach based on motion mode recognition using a smartphone. The motion mode consists of pedestrian movement state and phone pose. With the support vector machine (SVM) and the decision tree (DT), the arbitrary combinations of movement state and phone pose can be recognized successfully. In the traditional principal component analysis based (PCA-based) method, the obtained horizontal accelerations in one stride time interval cannot be guaranteed to be horizontal and the pedestrian’s direction vector will be influenced. To solve this problem, we propose a PCA-based method with global accelerations (PCA-GA) to infer pedestrian’s headings. Besides, based on the further analysis of phone poses, an ambiguity elimination method is also developed to calibrate the obtained headings. The results indicate that the recognition accuracy of the combinations of movement states and phone poses can be 92.4%. The 50% and 75% absolute estimation errors of pedestrian’s headings are 5.6° and 9.2°, respectively. This novel PCA-GA based method can achieve higher accuracy than traditional PCA-based method and heading offset method. The localization error can reduce to around 3.5 m in a trajectory of 164 m for different movement states and phone poses. MDPI 2018-06-04 /pmc/articles/PMC6021937/ /pubmed/29867027 http://dx.doi.org/10.3390/s18061811 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Boyuan Liu, Xuelin Yu, Baoguo Jia, Ruicai Gan, Xingli Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone |
title | Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone |
title_full | Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone |
title_fullStr | Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone |
title_full_unstemmed | Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone |
title_short | Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone |
title_sort | pedestrian dead reckoning based on motion mode recognition using a smartphone |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6021937/ https://www.ncbi.nlm.nih.gov/pubmed/29867027 http://dx.doi.org/10.3390/s18061811 |
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