Feature extraction for human activity recognition on streaming data

An online recognition system must analyze the changes in the sensing data and at any significant detection; it has to decide if there is a change in the activity performed by the person. Such a system can use the previous sensor readings for decision-making (decide which activity is performed), without the need to wait for future ones. This paper proposes an approach of human activity recognition on online sensor data. We present four methods used to extract features from the sequence of sensor events. Our experimental results on public smart home data show an improvement of effectiveness in classification accuracy.