Abstract: Public demand for intelligent services in their home environments can be expected to grow in the near future once the required technology becomes more widely available and mature. Many intelligent home services cannot be provided in a purely reactive fashion though since they require contextual knowledge about the environment and most importantly the activities the residents are engaged in at any given time. This poses a problem since information about a human’s behavior is not easily accessible and has to be recognized from aggregated sensor data in most cases. Numerous activity recognition techniqueshave been studied in the literature. In this thesis we focus on one such technique which takes a temporal reasoning approach to activity recognition, namely recognizing activities by planning for them with a temporal planner. OMPS is an example of such a planner that has been used in previous work to recognize activities of humans in domestic environments. An important requirement for monitoring activities in a real world application is the ability to do so continuously and reliably. Two shortcomings in the previous approach hindered OMPS’s capability to meet this requirement, namely maintaining the performance of the activity recognition over long monitoring horizons, and ensuring future temporal consistency of recognized activities. This thesis will define the two problems, detail their solutions, and finally evaluate the modified system with the corresponding changes implemented.