Developing intelligent systems towards automated clinical monitoring and assis-tance for the elderly is attracting significant attention. Age-related demographic trends in most western countries and increasing health-care costs, indicate a need for robust telehealth solutions which shall prolong seniors’ independent living. A key requirement in most ambient intelligence and assisted living applications is unobtrusiveness: Monitoring should not intervene with daily activities, so that the user feels comfortable and sensor data are collected naturally and in an unbiased fashion. Thus, in order to increase confidence in the representation of the user status it is necessary to exploit the existing monitoring equipment as much as possible, by combining different sensor resources, while addressing uncertainty. To this end, we employ a Complex Event Recognition methodology, which allows to combine heterogeneous data sources by means of event hierarchies. Our approach is based on a probabilistic version of the Event Calculus, which allows for handling noise and modelling uncertainty.