Physical activity (PA) level is a key element in the prevention and the treatment of several chronic diseases. In adults, low PA levels and sedentary behaviors are linked to higher risks of developing cardiovascular diseases (e.g. high blood pressure), type 2 diabetes and several types of cancers. In addition, PA is an essential component for the treatment of obese people and for the prevention of elderly’s loss of physical autonomy. In this context, the SVELTE project objectives were to develop new solutions to objectively measure one’s fitness level on one hand, identify a subject’s PA behavior in day-life conditions and its associated energy expenditure on the other hand. A few prototypes (including a multi-captor device linked with 3-axial accelerometer and magnetometer) were developed. The prototypes can store several days of data, are non-intrusive and easy to use for the subject. Several annotated databases have been collected and used to develop algorithms. These algorithms can (i) assess the overall fitness level of a subject, (ii) identify postures and types of PA performed throughout the day, and (iii) estimate the energy expenditure related to this daily PA. A clinical study protocol involving 120 subjects (obese and non-obese) wearing the sensors in their everyday life during 2weeks is ongoing and has the purpose to validate the performances of the energy expenditure algorithm.