Reducing computation costs for activity and gesture recognition using a tree-structured ensemble classifier

This paper proposes a new method that can recognize both activities and gestures by using acceleration data. While both activity recognition techniques and gesture recognition techniques employ acceleration data, these techniques are studied independently due to the large difference between the characteristics of activity sensor data and gesture sensor data. In this study, we combine their recognition using several weak classifiers that are widely used to recognize activities and/or gestures (e.g., FFT-based and DTW-based classifiers).