NeuralIO: Indoor outdoor detection via multimodal sensor data fusion on smartphones

Long Wang, Lennard Sommer, 黄逸然, Yexu Zhou, Till Riedel, Michael Beigl
July, 2020
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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.

Type
Conference paper
Publication
In International Summit Smart City 360°
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Indoor outdoor detection Multimodal data fusion Neural network model
黄逸然
黄逸然
Academic Associates

My research interests include Data Mining, XAI and Human Activity Recognition.