Human activity recognition from wearable sensors using extremely randomized trees

Learning and recognizing the physical activities of human based on wearable sensor has a wide range of applications in many fields such as assistive healthcare and security surveillance. In this paper, we propose an activity recognition framework based on extremely randomized trees and guided random forest to recognize both simple and complex activities from wearable sensor. In order to recognize different activities using extremely randomized trees, we first select most important features from all available features applying the feature selection method, namely, guided random forest; then, selected features are used to build the classifier for classifying activities. The proposed framework is extremely efficient in terms of recognition performance and computational time as it can recognize both small and large set of activities very accurately with different number of features in different sensor settings, while it needs fairly small amount of time for training and classification. The evaluation results of the experiments conducted on four benchmark data sets indicate that the proposed technique performs better than the classic activity recognition systems with respect to recognition accuracy and computational time; the proposed approach yielded the maximum recognition rate of 99.6%.