Societies need to devise mechanisms of caring for the well aging of the increasing number of seniors, as it is very important for elderly people to maintain their independence. Smart environments are being devised as a form of care in what has been called ambient assisted living. A smart environment should be able to respond in case of emergency or risk and inform any abnormal behavior. Still, not much research is done to understand behavior patterns, temporal changes and other particularities that can affect the effectiveness of smart environments in ambient assisted living. We explored the behavior of two adults in a smart environment in order to reveal temporal, spatial and sequential relations among the activities as well as the changes that these relations undergo overtime and across individuals. This paper presents an analysis of three human behavior patterns: temporal, location and frequency. These patterns are mined on two experimental subjects using the dataset provided by the CASAS project. Our analysis evidences how temporal, spatial and sequential patterns differ from person to person, day to day and after some time. Learning personalized behaviors and identifying and adapting to changes is a crucial aspect for smart environments since one-fit-all solutions are not suitable.