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Smartphone Location Recognition with Unknown Modes in Deep Feature Space

Smartphone location recognition aims to identify the location of a smartphone on a user in specific actions such as talking or texting. This task is critical for accurate indoor navigation using pedestrian dead reckoning. Usually, for that task, a supervised network is trained on a set of defined us...

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Detalles Bibliográficos
Autores principales: Daniel, Nati, Goldberg, Felix, Klein, Itzik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309937/
https://www.ncbi.nlm.nih.gov/pubmed/34300554
http://dx.doi.org/10.3390/s21144807
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author Daniel, Nati
Goldberg, Felix
Klein, Itzik
author_facet Daniel, Nati
Goldberg, Felix
Klein, Itzik
author_sort Daniel, Nati
collection PubMed
description Smartphone location recognition aims to identify the location of a smartphone on a user in specific actions such as talking or texting. This task is critical for accurate indoor navigation using pedestrian dead reckoning. Usually, for that task, a supervised network is trained on a set of defined user modes (smartphone locations), available during the training process. In such situations, when the user encounters an unknown mode, the classifier will be forced to identify it as one of the original modes it was trained on. Such classification errors will degrade the navigation solution accuracy. A solution to detect unknown modes is based on a probability threshold of existing modes, yet fails to work with the problem setup. Therefore, to identify unknown modes, two end-to-end ML-based approaches are derived utilizing only the smartphone’s accelerometers measurements. Results using six different datasets shows the ability of the proposed approaches to classify unknown smartphone locations with an accuracy of 93.12%. The proposed approaches can be easily applied to any other classification problems containing unknown modes.
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spelling pubmed-83099372021-07-25 Smartphone Location Recognition with Unknown Modes in Deep Feature Space Daniel, Nati Goldberg, Felix Klein, Itzik Sensors (Basel) Article Smartphone location recognition aims to identify the location of a smartphone on a user in specific actions such as talking or texting. This task is critical for accurate indoor navigation using pedestrian dead reckoning. Usually, for that task, a supervised network is trained on a set of defined user modes (smartphone locations), available during the training process. In such situations, when the user encounters an unknown mode, the classifier will be forced to identify it as one of the original modes it was trained on. Such classification errors will degrade the navigation solution accuracy. A solution to detect unknown modes is based on a probability threshold of existing modes, yet fails to work with the problem setup. Therefore, to identify unknown modes, two end-to-end ML-based approaches are derived utilizing only the smartphone’s accelerometers measurements. Results using six different datasets shows the ability of the proposed approaches to classify unknown smartphone locations with an accuracy of 93.12%. The proposed approaches can be easily applied to any other classification problems containing unknown modes. MDPI 2021-07-14 /pmc/articles/PMC8309937/ /pubmed/34300554 http://dx.doi.org/10.3390/s21144807 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Daniel, Nati
Goldberg, Felix
Klein, Itzik
Smartphone Location Recognition with Unknown Modes in Deep Feature Space
title Smartphone Location Recognition with Unknown Modes in Deep Feature Space
title_full Smartphone Location Recognition with Unknown Modes in Deep Feature Space
title_fullStr Smartphone Location Recognition with Unknown Modes in Deep Feature Space
title_full_unstemmed Smartphone Location Recognition with Unknown Modes in Deep Feature Space
title_short Smartphone Location Recognition with Unknown Modes in Deep Feature Space
title_sort smartphone location recognition with unknown modes in deep feature space
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309937/
https://www.ncbi.nlm.nih.gov/pubmed/34300554
http://dx.doi.org/10.3390/s21144807
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