Body Sensor Networks aim to capture the state of the user and its environment by utilizing from information heterogeneous sensors, and allow continuous monitoring of numerous physiological signals, where these sensors are attached to the subject's body. This can be immensely useful in activity recognition for identity verification, health and ageing and sport and exercise monitoring applications. In this paper, the application of body sensor networks for automatic and intelligent daily activity monitoring for elderly people, using wireless body sensors and smartphone inertial sensors has been reported. The scheme uses information theory-based feature ranking algorithms and classifiers based on random forests, ensemble learning and lazy learning. Extensive experiments using different publicly available datasets of human activity show that the proposed approach can assist in the development of intelligent and automatic real time human activity monitoring technology for eHealth application scenarios for elderly, disabled and people with special needs.