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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 32, Number 1(1) (2020)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 1-12
S&M2083 Research Paper of Special Issue https://doi.org/10.18494/SAM.2020.2586 Published: January 9, 2020 NeuralIO: Indoor–Outdoor Detection via Multimodal Sensor Data Fusion on Smartphones [PDF] Long Wang, Lennard Sommer, Yexu Zhou, Yiran Huang, Jingsi Wang, Till Riedel, and Michael Beigl (Received August 31, 2019; Accepted November 5, 2019) Keywords: indoor–outdoor detection, multimodal data fusion, neural network model
The indoor–outdoor (IO) status of mobile devices is fundamental information for various smart city applications. In this paper, we present NeuralIO, a neural-network-based method for dealing with the IO detection problem for smartphones. Multimodal data from various sensors on a smartphone are fused through neural network models to determine the IO status. A data set containing more than one million labeled samples is then constructed. We test the performance of an early fusion scheme in various settings. NeuralIO achieves an accuracy above 98% in 10-fold cross-validation and an accuracy above 90% in a real-world test.
Corresponding author: Long Wang  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Long Wang, Lennard Sommer, Yexu Zhou, Yiran Huang, Jingsi Wang, Till Riedel, and Michael Beigl, NeuralIO: Indoor–Outdoor Detection via Multimodal Sensor Data Fusion on Smartphones, Sens. Mater., Vol. 32, No. 1, 2020, p. 1-12. |
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