Human activity recognition on raw sensor data via sparse approximation

Human physical activity monitoring is a relatively new problem drawing much attention over the last years due to its wide application in medicine, homecare systems, prisoner monitoring etc. This paper presents Orthogonal Matching Pursuit based classifier as a method for activity recognition and proposes a modification to the classifier that significantly increases recognition accuracy. Both methods show promising results in both total recognition and differentiation between certain activities achieving up to 99.60% recognition accuracy even without any prior data processing. A comparison with other methods is also provided.