Research Article
NeuralIO: Indoor Outdoor Detection via Multimodal Sensor Data Fusion on Smartphones
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@INPROCEEDINGS{10.1007/978-3-030-51005-3_13, author={Long Wang and Lennard Sommer and Till Riedel and Michael Beigl and Yexu Zhou and Yiran Huang}, title={NeuralIO: Indoor Outdoor Detection via Multimodal Sensor Data Fusion on Smartphones}, proceedings={Science and Technologies for Smart Cities. 5th EAI International Summit, SmartCity360, Braga, Portugal, December 4-6, 2019, Proceedings}, proceedings_a={SMARTCITY}, year={2020}, month={7}, keywords={Indoor outdoor detection Multimodal data fusion Neural network model}, doi={10.1007/978-3-030-51005-3_13} }- Long Wang
Lennard Sommer
Till Riedel
Michael Beigl
Yexu Zhou
Yiran Huang
Year: 2020
NeuralIO: Indoor Outdoor Detection via Multimodal Sensor Data Fusion on Smartphones
SMARTCITY
Springer
DOI: 10.1007/978-3-030-51005-3_13
Abstract
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 to deal with the Indoor Outdoor (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 consisting of more than 1 million samples is constructed. We test the performance of an early fusion scheme in various settings. NeuralIO achieves above 98% accuracy in 10-fold cross-validation and above 90% accuracy in a real-world test.
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