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NeuralIO: Indoor-Outdoor Detection via Multimodal Sensor Data Fusion on Smartphones

Wang, Long; Sommer, Lennard; Zhou, Yexu; Huang, Yiran; Wang, Jingsi; Riedel, Till; Beigl, Michael

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.

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Verlagsausgabe §
DOI: 10.5445/IR/1000105135
Veröffentlicht am 20.01.2020
DOI: 10.18494/SAM.2020.2586
Web of Science
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Telematik (TM)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 01.2020
Sprache Englisch
Identifikator ISSN: 0914-4935
KITopen-ID: 1000105135
Erschienen in Sensors and materials
Verlag MYU
Band 32
Heft 1
Seiten Article: 1
Schlagwörter indoor–outdoor detection, multimodal data fusion, neural network model
Nachgewiesen in Web of Science
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