This article investigates new steps for the implementation and the utilization of presence indicators to identify displacement activities of a person and to model their life habits. This modeling is based on preliminary measures of displacement rate variations over a period of time. A possible time slot division of activities is highlighted by the analysis of these measures, slight variations of slot boundaries could appear from day to day, from person to person. On this basis, we show how to build three new indicators by working from time slot to time slot, in order to detect alerts or drifts from "normal" behavior as soon as possible and in a more reliable way. These indicators include start and end times of time slots, displacement rate and duration of each time slot. The algorithm we propose has been tested in real situations to show its use and relevance. Results are finally integrated in a more ambitious process of detection and automatic decision-making support through the conception of a web interface.