S&M2083 Research Paper of Special Issue
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.