The characterization of human behavior based on the analysis of their activity requires tools that assure the reliability of the collected data. In the last few years, and due to the electronics evolution and the success of personal communication devices (smartphones, tablets, etc.), data collection initiatives on human interaction based on such devices have proliferated. These initiatives make use of the communication capabilities of personal devices to estimate the proximity of people who carry them. However, these devices have not been designed for this purpose, and consequently, they suffer from a set of limitations that, if they are not properly addressed, questions the quality of the collected data and their reliability for the characterization of human behavior.

The first part of the thesis analyzes the limitations of previous proximity data collection initiatives using personal communication devices, and presents a Bluetooth-based ad hoc system that considers them all, and implements a set of mechanisms to solve them when possible, or at least, minimize their impact. Its operation is described and its performance in a real deployment is presented, with a resultant high reliability database at the disposal of the public research community. The results of the processing stage show important differences with the conclusions of previous initiatives. In order to fully understand human behavior, it is necessary to consider the conditions, the context where they carry out their daily activities. The locations where people stay at every moment are a very important part of the context.

The second part of the thesis is focused on the development of a tool that provides localization information using the data provided by the system developed in the first part of the thesis: the information related to human proximity and their mobility. This tool takes the form of a family of localization algorithms that is validated through a set of experiments in different scenarios with multiple topologies. Finally, the applicability of this tool is shown on a sample of the real database obtained from the _rst part of the thesis, where the proximity and mobility information from the people in the experiment becomes information about their localization.