Elderly people can be monitored at home to detect autonomy issues in their behavior. In addition to the environmental sensors (presence and movements in a room, temperature in the flat, light, etc.), we developed an inertial and magnetic based central of sensors to monitor the activity of the person. This article presents a wavelet-based pattern recognition algorithm that work on the data of this central to detect the postural transitions occurring in the daily life. We constructed four patterns: (1) for stand to sit, (2) for sit to stand, (3) for stand to lying down and (4) for lying down to stand. With this, we are able to detect theses changes, and to infer (as we consider that the subject is stand-up when the sensor is turned on) its posture. We also have, with these sensors, an idea of the activity of the person in each frame of time (immobile, moving, etc.). To test this algorithm and verify that the patterns are independent of the subject, we asked fifteen people to reproduce a scenario and we present, in the last section of this article, the results obtained. Results of an experiment are also given to show a mean good classification rate of 70% for this method.