Robust Smartphone Mode Recognition

Itzik Klein; Yuval Solaz; Rotem Alaluf

doi:10.1109/ICSEE.2018.8646011

Robust Smartphone Mode Recognition

Itzik Klein, Yuval Solaz, Rotem Alaluf

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Smartphone based pedestrian dead reckoning (PDR) approach is commonly used for indoor positioning. Recognition of the smartphone mode can improve PDR positioning accuracy. In this paper, we employ machine learning classification algorithms to recognize the smartphone modes (e.g. pocket or swing) and thereby enabling the choice of a proper gain value to improve PDR positioning accuracy. In particular, we focus on two classification approaches: 1) tree based approaches: random forest, gradient boosting and CatBoost 2) neural network approaches: convolutional neural network, recurrent neural networks with long short-Term memory units, gated recurrent unit and residual recurrent neural networks. Experimental results obtained using thirteen participates walking in an inhomogeneous environments and smartphone modes show successes of more than 97% in classifying the smartphone modes using neural network approaches.

Original language	English
Title of host publication	2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538663783
DOIs	https://doi.org/10.1109/ICSEE.2018.8646011
State	Published - 2 Jul 2018
Externally published	Yes
Event	2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 - Eilat, Israel Duration: 12 Dec 2018 → 14 Dec 2018

Publication series

Name	2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018

Conference

Conference	2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
Country/Territory	Israel
City	Eilat
Period	12/12/18 → 14/12/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ICSEE.2018.8646011

Cite this

Klein, I., Solaz, Y., & Alaluf, R. (2018). Robust Smartphone Mode Recognition. In 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 Article 8646011 (2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSEE.2018.8646011

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title = "Robust Smartphone Mode Recognition",

abstract = "Smartphone based pedestrian dead reckoning (PDR) approach is commonly used for indoor positioning. Recognition of the smartphone mode can improve PDR positioning accuracy. In this paper, we employ machine learning classification algorithms to recognize the smartphone modes (e.g. pocket or swing) and thereby enabling the choice of a proper gain value to improve PDR positioning accuracy. In particular, we focus on two classification approaches: 1) tree based approaches: random forest, gradient boosting and CatBoost 2) neural network approaches: convolutional neural network, recurrent neural networks with long short-Term memory units, gated recurrent unit and residual recurrent neural networks. Experimental results obtained using thirteen participates walking in an inhomogeneous environments and smartphone modes show successes of more than 97% in classifying the smartphone modes using neural network approaches.",

author = "Itzik Klein and Yuval Solaz and Rotem Alaluf",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 ; Conference date: 12-12-2018 Through 14-12-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/ICSEE.2018.8646011",

language = "English",

series = "2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018",

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Klein, I, Solaz, Y & Alaluf, R 2018, Robust Smartphone Mode Recognition. in 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018., 8646011, 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018, Eilat, Israel, 12/12/18. https://doi.org/10.1109/ICSEE.2018.8646011

Robust Smartphone Mode Recognition. / Klein, Itzik; Solaz, Yuval; Alaluf, Rotem.
2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8646011 (2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Smartphone based pedestrian dead reckoning (PDR) approach is commonly used for indoor positioning. Recognition of the smartphone mode can improve PDR positioning accuracy. In this paper, we employ machine learning classification algorithms to recognize the smartphone modes (e.g. pocket or swing) and thereby enabling the choice of a proper gain value to improve PDR positioning accuracy. In particular, we focus on two classification approaches: 1) tree based approaches: random forest, gradient boosting and CatBoost 2) neural network approaches: convolutional neural network, recurrent neural networks with long short-Term memory units, gated recurrent unit and residual recurrent neural networks. Experimental results obtained using thirteen participates walking in an inhomogeneous environments and smartphone modes show successes of more than 97% in classifying the smartphone modes using neural network approaches.

AB - Smartphone based pedestrian dead reckoning (PDR) approach is commonly used for indoor positioning. Recognition of the smartphone mode can improve PDR positioning accuracy. In this paper, we employ machine learning classification algorithms to recognize the smartphone modes (e.g. pocket or swing) and thereby enabling the choice of a proper gain value to improve PDR positioning accuracy. In particular, we focus on two classification approaches: 1) tree based approaches: random forest, gradient boosting and CatBoost 2) neural network approaches: convolutional neural network, recurrent neural networks with long short-Term memory units, gated recurrent unit and residual recurrent neural networks. Experimental results obtained using thirteen participates walking in an inhomogeneous environments and smartphone modes show successes of more than 97% in classifying the smartphone modes using neural network approaches.

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M3 - Conference contribution

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Robust Smartphone Mode Recognition

Abstract

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ASJC Scopus subject areas

Access to Document

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