Limits...
MagicFinger: 3D Magnetic Fingerprints for Indoor Location.

Carrillo D, Moreno V, Úbeda B, Skarmeta AF - Sensors (Basel) (2015)

Bottom Line: The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy.Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors.These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m).

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Communications Engineering, University of Murcia, 30100 Murcia, Spain. daniel.carrillo2@um.es.

ABSTRACT
Given the indispensable role of mobile phones in everyday life, phone-centric sensing systems are ideal candidates for ubiquitous observation purposes. This paper presents a novel approach for mobile phone-centric observation applied to indoor location. The approach involves a location fingerprinting methodology that takes advantage of the presence of magnetic field anomalies inside buildings. Unlike existing work on the subject, which uses the intensity of magnetic field for fingerprinting, our approach uses all three components of the measured magnetic field vectors to improve accuracy. By using adequate soft computing techniques, it is possible to adequately balance the constraints of common solutions. The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy. The proposed system consists of two phases: the offline phase and the online phase. In the offline phase, magnetic field measurements are taken throughout the building, and 3D maps are generated. Then, during the online phase, the user's location is estimated through the best estimator for each zone of the building. Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors. These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m).

No MeSH data available.


Related in: MedlinePlus

Classification results of automatically-clustered data in Building A.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4541928&req=5

f6-sensors-15-17168: Classification results of automatically-clustered data in Building A.

Mentions: The results are summarized in Figure 6. We assess the performance with the f1-score metric, whose expression is , and whose value range is 0 <= F1 <= 1. Even though it does not consider the true negative rate, it is possible to see whether each class (i.e., zone) has been correctly predicted or not with respect to the other classes, but this is underestimated. As can be seen in Figure 6, the classifier based on Gaussian processes yields more stable results and, unlike the others, is able to predict five zones out of eight with an f1-score higher than 0.5. Furthermore, classification is improved if based on feature selection rather than on intensities alone, unlike in the clustering stage. The variables selected are the following: Bx, By, Bz intensity; By kurtosis; Bx, By, Bz skewness; Bx, Bz SumPowerDetCoeff; Bz VarFFT.


MagicFinger: 3D Magnetic Fingerprints for Indoor Location.

Carrillo D, Moreno V, Úbeda B, Skarmeta AF - Sensors (Basel) (2015)

Classification results of automatically-clustered data in Building A.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541928&req=5

f6-sensors-15-17168: Classification results of automatically-clustered data in Building A.
Mentions: The results are summarized in Figure 6. We assess the performance with the f1-score metric, whose expression is , and whose value range is 0 <= F1 <= 1. Even though it does not consider the true negative rate, it is possible to see whether each class (i.e., zone) has been correctly predicted or not with respect to the other classes, but this is underestimated. As can be seen in Figure 6, the classifier based on Gaussian processes yields more stable results and, unlike the others, is able to predict five zones out of eight with an f1-score higher than 0.5. Furthermore, classification is improved if based on feature selection rather than on intensities alone, unlike in the clustering stage. The variables selected are the following: Bx, By, Bz intensity; By kurtosis; Bx, By, Bz skewness; Bx, Bz SumPowerDetCoeff; Bz VarFFT.

Bottom Line: The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy.Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors.These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m).

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Communications Engineering, University of Murcia, 30100 Murcia, Spain. daniel.carrillo2@um.es.

ABSTRACT
Given the indispensable role of mobile phones in everyday life, phone-centric sensing systems are ideal candidates for ubiquitous observation purposes. This paper presents a novel approach for mobile phone-centric observation applied to indoor location. The approach involves a location fingerprinting methodology that takes advantage of the presence of magnetic field anomalies inside buildings. Unlike existing work on the subject, which uses the intensity of magnetic field for fingerprinting, our approach uses all three components of the measured magnetic field vectors to improve accuracy. By using adequate soft computing techniques, it is possible to adequately balance the constraints of common solutions. The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy. The proposed system consists of two phases: the offline phase and the online phase. In the offline phase, magnetic field measurements are taken throughout the building, and 3D maps are generated. Then, during the online phase, the user's location is estimated through the best estimator for each zone of the building. Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors. These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m).

No MeSH data available.


Related in: MedlinePlus