Semi-supervised learning: predicting activities in Android environment

Predicting activities from data gathered with sensors gained importance over the years with the objective of getting a better understanding of the human body. The purpose of this paper is to show that predicting activities on an Android phone is possible. We take into consideration different classifiers, their accuracy using different approaches (hierarchical and one step classification) and limitations of the mobile itself like battery and memory usage. A semi-supervised learning approach is taken in order to compare its results against supervised learning. The objective is to discover if the application can be adapted to the user providing a better solution for this problem. The activities predicted are the most usual in everyday life: walking, running, standing idle and sitting. An android prototype, embedding the software MOA, was developed to experimentally evaluate the ideas proposed here.