Abstract
Automatic activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors, and permit continuous monitoring of numerous physiological signals, where these sensors are attached to the subject's body. This can be immensely useful in healthcare applications, for automatic and intelligent daily activity monitoring for elderly people. In this paper, we present a novel data analytic scheme for intelligent Human Activity Recognition (AR) using wireless body sensors and smartphone inertial sensors which use information theory-based feature ranking algorithms and classifiers based on random forests, ensemble learning and lazy learning. Further, we propose a novel multimodal scheme based on combining multimodal three dimensional (x, y, z) accelerometer and gyro data from smart phone inertial sensors. Extensive experiments using different publicly available database 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.