In recent years there has been growing interest in solutions for the delivery of clinical care for the elderly, due to the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but, to be useful, this activity must not be too invasive for patients and a burden for caregivers. In this chapter we want to consider how knowledge representation and reasoning techniques can be used in sensory-rich environments, where data about the person and the environment conditions are collected through pervasive Wireless Sensor Networks (WSN), and expressive knowledge representation and reasoning techniques can be used to combine these data with external knowledge sources at a symbolic level to support caregivers in understanding patients' well being and in predicting possible evolutions of their health. A hierarchical logic-based model of health is able to combine data from different sources, sensor data, tests results, commonsense knowledge and patient's clinical profile at the lower level, and correlation rules between health conditions across upper levels. The logical formalization and the reasoning process are based on non-monotonic reasoning and the implementation and testing of components is illustrated in the framework of Answer Set Programming. The expressive power of this logic programming paradigm makes it possible to reason about contextual aspects of health evolution even when the available information is incomplete and potentially incoherent, while declarativity simplifies rules specification by caregivers and allows automatic encoding of knowledge. Assuming the presence of such a complex and heterogenous intelligent monitoring system equipped with a pervasive sensor network and a non-monotonic reasoning engine, the rich set of sensors that can be used for monitoring in home environments and their sheer number make it quite complex to provide a correct interpretation of collected data for a particular patient. For this reason, we can take advantage of a logic-based context model for situation assessment, and we use logic programming techniques to reason about different pieces of knowledge for prevention of risky situation and proactive reaction of the system via appropriate policy rules expressed in an event-condition-action style and enforced via reasoning. A concrete example of how this functionality may represent a key aspect of assistive technology is represented by fall prevention for the elderly. This aspect will be illustrated in the chapter as a concrete application of the proposed methodologies.