Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1922
Title: Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks
Authors: Galván Tejada, Carlos Eric
Zanella Calzada, Laura Alejandra
García Dominguez, Antonio
Magallanes Quintanar, Rafael
Luna García, Huizilopoztli
Celaya Padilla, José
Galván Tejada, Jorge
Vélez Rodríguez, Alberto
Gamboa Rosales, Hamurabi
Issue Date: 14-Apr-2020
Publisher: MDPI
Abstract: Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.
Description: Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.
URI: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1922
https://doi.org/10.48779/4kqm-bw37
ISSN: 2220-9964
Other Identifiers: info:eu-repo/semantics/publishedVersion
Appears in Collections:*Documentos Académicos*-- M. en Ciencias del Proc. de la Info.

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