In this thesis we present two e-health applications. We begin with a practical real time fall detection system running on a smartwatch called F2D. In F2D data from the accelerometer is collected, passing through an adaptive threshold-based algorithm which detects patterns corresponding to a fall. A decision module takes into account the residual movement of the user, matching a detected fall pattern to an actual fall. To the best of our knowledge, this is the first fall detection system which works on a smartwatch, being less stigmatizing for the end user. The next implementation of our expertise and second main element of this thesis is the detection of stress patterns by analyzing smartphone data. We present a novel stress detection system which aims to detect stress and burn-out risks by analyzing the behaviors of the users via their smartphone.