M. Luštrek

Abstract:

We used machine learning to recognize activities from four tags attached to the body. The coordinates of the tags were obtained with Ubisense real-time location system. We determined that the most suitable machine learning algorithm is Random Forest and that the attribute vector is best assembled from a string of attributes belonging to ten consecutive snapshots of tag coordinates. The main contribution of the paper are four methods for improvement of activity recognition, which take into account the time sequence of recognized activities. These methods improve the classification accuracy by 1.66 percentage points.