A Novel Step Length Estimator Based on Foot-Mounted MEMS Sensors
Pedestrian Dead Reckoning (PDR)-based pedestrian navigation technology is an important part of indoor and outdoor seamless positioning services. To improve the performance of PDR, we have conducted research on a step length estimator. Firstly, based on the basic theory of inertial navigation, we ana...
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/PMC6308666/ https://www.ncbi.nlm.nih.gov/pubmed/30558332 http://dx.doi.org/10.3390/s18124447 |
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author | Zhu, Zhuangsheng Wang, Shibo |
author_facet | Zhu, Zhuangsheng Wang, Shibo |
author_sort | Zhu, Zhuangsheng |
collection | PubMed |
description | Pedestrian Dead Reckoning (PDR)-based pedestrian navigation technology is an important part of indoor and outdoor seamless positioning services. To improve the performance of PDR, we have conducted research on a step length estimator. Firstly, based on the basic theory of inertial navigation, we analyze in detail the errors in traditional Strapdown Inertial Navigation Systems (SINSs) caused by the unique motion state of pedestrians. Then, according to the fact that the inertial data from the foot can directly reflect the gait characteristics, we conduct a step length estimator that does not rely on SINS. The experimental results show that accuracy of the proposed method is between 0.6% and 1.4% with a standard deviation of 0.25%. |
format | Online Article Text |
id | pubmed-6308666 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63086662019-01-04 A Novel Step Length Estimator Based on Foot-Mounted MEMS Sensors Zhu, Zhuangsheng Wang, Shibo Sensors (Basel) Article Pedestrian Dead Reckoning (PDR)-based pedestrian navigation technology is an important part of indoor and outdoor seamless positioning services. To improve the performance of PDR, we have conducted research on a step length estimator. Firstly, based on the basic theory of inertial navigation, we analyze in detail the errors in traditional Strapdown Inertial Navigation Systems (SINSs) caused by the unique motion state of pedestrians. Then, according to the fact that the inertial data from the foot can directly reflect the gait characteristics, we conduct a step length estimator that does not rely on SINS. The experimental results show that accuracy of the proposed method is between 0.6% and 1.4% with a standard deviation of 0.25%. MDPI 2018-12-15 /pmc/articles/PMC6308666/ /pubmed/30558332 http://dx.doi.org/10.3390/s18124447 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 Zhu, Zhuangsheng Wang, Shibo A Novel Step Length Estimator Based on Foot-Mounted MEMS Sensors |
title | A Novel Step Length Estimator Based on Foot-Mounted MEMS Sensors |
title_full | A Novel Step Length Estimator Based on Foot-Mounted MEMS Sensors |
title_fullStr | A Novel Step Length Estimator Based on Foot-Mounted MEMS Sensors |
title_full_unstemmed | A Novel Step Length Estimator Based on Foot-Mounted MEMS Sensors |
title_short | A Novel Step Length Estimator Based on Foot-Mounted MEMS Sensors |
title_sort | novel step length estimator based on foot-mounted mems sensors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308666/ https://www.ncbi.nlm.nih.gov/pubmed/30558332 http://dx.doi.org/10.3390/s18124447 |
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