Continuous pervasive monitoring has the potential to improve the quality of life of many persons (for example those who suffer from heart arrhythmias or elderly people who suffer from chronic diseases). We show the three main steps related to the monitoring process and supported by our system. First, how data are captured by sensors which send those data using wireless communications to a PDA (Personal Digital Assistant). Second, how data are analyzed, locally on the PDA, using techniques proposed in the areas of the Semantic Web and Machine Learning. Finally, how data stored at the PDA can be queried remotely using web services. Those three steps are illustrated in two different scenarios that the system can deal with: tele-assistance and monitoring of arrhythmias. Moreover, through the paper we highlight the main advantages provided by the system: active monitoring which consists in reacting to anomalous situations without direct intervention of the user; universal assistance, i.e. irrespective of time or place through the use of wireless communications and PDAs; vital signs monitoring which consists in using sensors that capture the value of those vital signs, and subsequently feed them to a decision support system that analyses them and generates an alarm if necessary; and remote monitoring which allows authorized personal to consult data on monitored patients, using the Internet. Finally we present some performance results which demonstrate the feasibility of the system.