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

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 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.

Smartphone Level Indoor/Outdoor Ubiquitous Pedestrian Positioning 3dma GNSS/Vins Integration Using Fgo

“…Hence, IO (Indoor/Outdoor) detection is the key to ubiquitous positioning. To accomplish the ubiquitous indoor localization framework, many researchers conducted IO detection using GPS measurement (Rajak et al, 2021a) (Pei et al, 2009) (Wang et al, 2020). The framework should not require any extra infrastructure but use the existing built-in sensors in the smartphone.…”