This paper presents a novel indoor tracking system built with common data mining techniques on radio frequency identification (RFID) tags readings. The system allows tracking of several objects in real-time in a smart home context and is a building block toward the deployment of an expert system to enable aging in place through technology. The indoor localization is modelled as a classification problem, instead of a regression problem as commonly seen in the literature. The paper is divided in two parts. The first one focuses on the ground truth collection that led to the model construction. The second part focuses on the filters that were designed to enable this model to be used in real-time in the smart home as a tracking software. Results from the first part show that most classifiers perform well on the static positioning of RFID tags task, with a random forest of 100 trees performing best at 97% accuracy and 0.9740974 F-Measure. However, collecting data to train the classifier is a long and tedious process. Results from the second part indicate that the accuracy of the random forest drops significantly when confronted with human interference. With the help of some filters, the tracking accuracy of objects can still be as high as 75%. Those results confirm that using passive RFID tags for an indoor tracking system is viable. Our system is easy to deploy and more flexible than trilateration or fingerprinting systems. (C) 2018 Elsevier Ltd. All rights reserved.
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