Physical activity is one important aspect in user behavior analysis. Abnormal movement behavior might be an indicator for an inappropriate lifestyle, insufficient social inclusion, or generally disadvantageous life conditions which might call for medical treatment. Assistive technologies can make use of information on the physical activity of e.g. residents of a nursing home or elderly patients living alone at home. In this paper, we present a mobile technology for identifying movement behavior in everyday life. A three-dimensional acceleration sensor is used to determine physical activity by domain specific feature extraction. By use of data mining techniques and a feature set extracted from everyday usage data, we achieve a high quality and robust classification of physical activity. This can be used for further user behavior analysis. Especially non-linear features like step-detection, horizontal and vertical acceleration as well as spectral analysis proved to be very powerful. A proof-of concept prototype is described which shows the applicability of the developed technologies in everyday life.