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Indoor localization is increasingly required for applications like deployment of rescue teams in emergency situations, proactive care for the elders, and so on. The quick growing of coverage of WiFi networks makes WiFi technology a very promising choice for indoor localization. But, this localization should be linked to a map to be useful. This work presents an open-access software designed for that purpose. It is composed of two different applications, a desktop software for research purposes and an Android application for user friendly localization. We address the localization task as a high dimensional classification problem. So far, we have developed classifiers based on the classic Nearest Neighbour, Support Vector Machines (SVM) and fuzzy rule-based classifiers. This work is made in the context of the ABSYNTHE project which is aimed at creating human-robot teams. We show a use case of the new software in one of the scenarios of the ABSYNTHE project.

This work presents the full integration of fuzzy inference-grams (Fingrams) in KEEL to visual analysis of fuzzy association rules. Fingrams graphically represent fuzzy rule-based systems (FRBSs) in 2D graphs that illustrate the interaction among fuzzy rules in terms of rule cofiring, i.e., paying attention to rule pairs simultaneous fired by a given input. The new module allows to generate Fingrams for fuzzy association rules created in the suite KEEL, that can be afterwards analyzed to comprehend the system behavior and improve it. We sketch the use and potentials in an illustrative example built in KEEL over a real-world dataset including qualitative assessments of a set of design chairs.