Feature Extraction Method for Real Time Human Activity Recognition on Cell Phones

Abstract

In this paper we contribute a novel linear-time method for extracting features from acceleration sensor signals in order to identify human activities. We benchmark this method using a standard acceleration-based activity recognition dataset called SCUT-NAA. The results show that the described method performs best when the training and testing data are from the same person. In this context, a linear kernel based support vector machine (SVM) classifier and a radial basis function (RBF) based one produced similar levels of accuracy. Finally we demonstrate an application of the proposed method for realtime activity recognition on a cell phone with a single triaxial accelerometer. This feature extraction method can be used for realtime activity recognition on resource constrained devices.