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...

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Detalles Bibliográficos
Autores principales: Zhu, Zhuangsheng, Wang, Shibo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
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%.
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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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