Cargando…

Smartphone Location Recognition: A Deep Learning-Based Approach

One of the approaches for indoor positioning using smartphones is pedestrian dead reckoning. There, the user step length is estimated using empirical or biomechanical formulas. Such calculation was shown to be very sensitive to the smartphone location on the user. In addition, knowledge of the smart...

Descripción completa

Detalles Bibliográficos
Autor principal: Klein, Itzik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6983022/
https://www.ncbi.nlm.nih.gov/pubmed/31905990
http://dx.doi.org/10.3390/s20010214
_version_ 1783491424009846784
author Klein, Itzik
author_facet Klein, Itzik
author_sort Klein, Itzik
collection PubMed
description One of the approaches for indoor positioning using smartphones is pedestrian dead reckoning. There, the user step length is estimated using empirical or biomechanical formulas. Such calculation was shown to be very sensitive to the smartphone location on the user. In addition, knowledge of the smartphone location can also help for direct step-length estimation and heading determination. In a wider point of view, smartphone location recognition is part of human activity recognition employed in many fields and applications, such as health monitoring. In this paper, we propose to use deep learning approaches to classify the smartphone location on the user, while walking, and require robustness in terms of the ability to cope with recordings that differ (in sampling rate, user dynamics, sensor type, and more) from those available in the train dataset. The contributions of the paper are: (1) Definition of the smartphone location recognition framework using accelerometers, gyroscopes, and deep learning; (2) examine the proposed approach on 107 people and 31 h of recorded data obtained from eight different datasets; and (3) enhanced algorithms for using only accelerometers for the classification process. The experimental results show that the smartphone location can be classified with high accuracy using only the smartphone’s accelerometers.
format Online
Article
Text
id pubmed-6983022
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-69830222020-02-06 Smartphone Location Recognition: A Deep Learning-Based Approach Klein, Itzik Sensors (Basel) Article One of the approaches for indoor positioning using smartphones is pedestrian dead reckoning. There, the user step length is estimated using empirical or biomechanical formulas. Such calculation was shown to be very sensitive to the smartphone location on the user. In addition, knowledge of the smartphone location can also help for direct step-length estimation and heading determination. In a wider point of view, smartphone location recognition is part of human activity recognition employed in many fields and applications, such as health monitoring. In this paper, we propose to use deep learning approaches to classify the smartphone location on the user, while walking, and require robustness in terms of the ability to cope with recordings that differ (in sampling rate, user dynamics, sensor type, and more) from those available in the train dataset. The contributions of the paper are: (1) Definition of the smartphone location recognition framework using accelerometers, gyroscopes, and deep learning; (2) examine the proposed approach on 107 people and 31 h of recorded data obtained from eight different datasets; and (3) enhanced algorithms for using only accelerometers for the classification process. The experimental results show that the smartphone location can be classified with high accuracy using only the smartphone’s accelerometers. MDPI 2019-12-30 /pmc/articles/PMC6983022/ /pubmed/31905990 http://dx.doi.org/10.3390/s20010214 Text en © 2019 by the author. 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
Klein, Itzik
Smartphone Location Recognition: A Deep Learning-Based Approach
title Smartphone Location Recognition: A Deep Learning-Based Approach
title_full Smartphone Location Recognition: A Deep Learning-Based Approach
title_fullStr Smartphone Location Recognition: A Deep Learning-Based Approach
title_full_unstemmed Smartphone Location Recognition: A Deep Learning-Based Approach
title_short Smartphone Location Recognition: A Deep Learning-Based Approach
title_sort smartphone location recognition: a deep learning-based approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6983022/
https://www.ncbi.nlm.nih.gov/pubmed/31905990
http://dx.doi.org/10.3390/s20010214
work_keys_str_mv AT kleinitzik smartphonelocationrecognitionadeeplearningbasedapproach