Life expectancy is nowadays increasing thanks to major improvements in medicine. Thus, modern societies are facing the great challenge to care after a fast growing population of elderly people. For that reason, researchers work on solutions to maintain, as long as possible, elderly persons safely in their own home, with efficient systems to detect abnormal trends and to launch alarms. This project deals with the development of indicators to detect the loss of autonomy. A flat was equipped with different sensors to classify the activities of daily living of the patient in his own environment. This paper describes the installation of the different sensors and the results of the preliminary individual evaluation of two of them (a sound and speech recognition system and an inertial/magnetic kinematic sensor). The first system classifies the sounds produced in the flat in eight classes and differentiates the normal sentences with the distress sentences uttered by the subject. The second analyzes the signal from the accelerometers and magnetometers to identify the posture and the level of activity. The algorithms were tested with two scenarios performed by ten subjects.