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 (i.e., 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.
Highlights
People’s indoor location estimation (ILE) is one of the most important context variables, since they are essential data for location-based services (LBS)
Magnetic field fingerprints can be viewed as bi-dimensional data: Even when magnetic field data is viewed as a unique data point, a collection of points of a room can be treated as a bi-dimensional data heatmap that allows us to develop an indoor location system (ILS) with bi-dimensional techniques
These bi-dimensional representations are viewed as a spectral evolution after applying an Fast Fourier Transform (FFT), as presented in the results section, which means that spectral information and their properties are present due to the above, it means that a partial fingerprint has enough information that can identify the room
Summary
People’s indoor location estimation (ILE) is one of the most important context variables, since they are essential data for location-based services (LBS) This allows us to increase the features of these services in many fields, such as, eHealth [1], shopping centers [2], automotive applications [3], among others. There are several proposals that use the propagation of signals, such as Bluetooth, Zigbee, WiFi and radio frequency identification (RFID), among others [5,6,7,8] These proposals have proved their ability to develop accurate and robust ILS, through the use of sensors and diverse devices, allowing the development of commercial and well-known systems, e.g., LANDMARC [9], Bluepos [10], CLIPS [11], to mention some [12,13]. The coverage of these systems has several constrains of infrastructure materials, point of view, signal propagation, etc., giving a limited coverage for the estimation of the location of the user
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