Human Activity Recognition: From Controlled Lab Experiments to Competitive Live Evaluation

Human activity recognition is a basic building block in numerous healthcare systems, mainly because the ability to understand the user´s situation and context. This paper presents a solution to the general problem with evaluation of human activity recognition systems, i.e., an activity recognition system may perform perfectly in controlled lab experiments, but significantly worse once applied to more realistic conditions. The solution is presented through the practical experience gained with the creation of our RAReFall activity recognition system. Although the system was awarded the first place at the EvAAL-AR live competition, the recognition accuracy at the competition was significantly lower compared to the controlled lab experiments performed just before the competition. To overcome the encountered problem we developed an automatic calibration method, which solves the encountered problem by adapting and re-calibrating the accelerometer data in real-time while the user is performing everyday activities. The method increased the overall accuracy for 8 percentage points and for 51 percentage points for the sitting activity.