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A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning

The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation....

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Autores principales: Zhu, Yida, Luo, Haiyong, Wang, Qu, Zhao, Fang, Ning, Bokun, Ke, Qixue, Zhang, Chen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412305/
https://www.ncbi.nlm.nih.gov/pubmed/30769914
http://dx.doi.org/10.3390/s19040786
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author Zhu, Yida
Luo, Haiyong
Wang, Qu
Zhao, Fang
Ning, Bokun
Ke, Qixue
Zhang, Chen
author_facet Zhu, Yida
Luo, Haiyong
Wang, Qu
Zhao, Fang
Ning, Bokun
Ke, Qixue
Zhang, Chen
author_sort Zhu, Yida
collection PubMed
description The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions. In this paper, we consider the challenge of switching quickly in IO transition regions with high detection accuracy in complex scenarios. Towards this end, we analyze and extract spatial geometry distribution, time sequence and statistical features under different sliding windows from GNSS measurements in Android smartphones and present a novel IO detection method employing an ensemble model based on stacking and filtering the detection result by Hidden Markov Model. We evaluated our algorithm on four datasets. The results showed that our proposed algorithm was capable of identifying IO state with 99.11% accuracy in indoor and outdoor environment where we have collected data and 97.02% accuracy in new indoor and outdoor scenarios. Furthermore, in the scenario of indoor and outdoor transition where we have collected data, the recognition accuracy reaches 94.53% and the probability of switching delay within 3 s exceeds 80%. In the new scenario, the recognition accuracy reaches 92.80% and the probability of switching delay within 4 s exceeds 80%.
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spelling pubmed-64123052019-04-03 A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning Zhu, Yida Luo, Haiyong Wang, Qu Zhao, Fang Ning, Bokun Ke, Qixue Zhang, Chen Sensors (Basel) Article The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions. In this paper, we consider the challenge of switching quickly in IO transition regions with high detection accuracy in complex scenarios. Towards this end, we analyze and extract spatial geometry distribution, time sequence and statistical features under different sliding windows from GNSS measurements in Android smartphones and present a novel IO detection method employing an ensemble model based on stacking and filtering the detection result by Hidden Markov Model. We evaluated our algorithm on four datasets. The results showed that our proposed algorithm was capable of identifying IO state with 99.11% accuracy in indoor and outdoor environment where we have collected data and 97.02% accuracy in new indoor and outdoor scenarios. Furthermore, in the scenario of indoor and outdoor transition where we have collected data, the recognition accuracy reaches 94.53% and the probability of switching delay within 3 s exceeds 80%. In the new scenario, the recognition accuracy reaches 92.80% and the probability of switching delay within 4 s exceeds 80%. MDPI 2019-02-14 /pmc/articles/PMC6412305/ /pubmed/30769914 http://dx.doi.org/10.3390/s19040786 Text en © 2019 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, Yida
Luo, Haiyong
Wang, Qu
Zhao, Fang
Ning, Bokun
Ke, Qixue
Zhang, Chen
A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
title A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
title_full A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
title_fullStr A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
title_full_unstemmed A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
title_short A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
title_sort fast indoor/outdoor transition detection algorithm based on machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412305/
https://www.ncbi.nlm.nih.gov/pubmed/30769914
http://dx.doi.org/10.3390/s19040786
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