KIT | KIT-Bibliothek | Impressum | Datenschutz

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

Wang, Long 1; Sommer, Lennard 1; Zhou, Yexu 1; Huang, Yiran ORCID iD icon 1; Wang, Jingsi 1; Riedel, Till ORCID iD icon 1; Beigl, Michael 1
1 Karlsruher Institut für Technologie (KIT)


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.

Verlagsausgabe §
DOI: 10.5445/IR/1000105135
Veröffentlicht am 20.01.2020
DOI: 10.18494/SAM.2020.2586
Web of Science
Zitationen: 2
Zitationen: 2
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 Dimensions
Web of Science
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page