Unsupervised detection of transitions at home by using BLE technology

This paper presents a set of unsupervised methods based on Bluetooth Low Energy (BLE) technology to obtain the position and transitions that users perform between the zones where they usually stay longer in their own homes. In particular, two different methods are studied to detect the user's position, the first based on proximity and the second based on fingerprinting techniques with self-training. The proposed methodology allows a very easy-to-use deployment by the user and with a very low economic cost. A set of experiments have been carried out to assess the performance of the proposed techniques presented in this paper, where a real user has captured data for several days at home. The results obtained show that the fingerprinting-based method is the most effective to accurately detect the user's position and the transitions performed between the different areas of interest at home.