Document Type

Conference Proceeding



Publication Date


Source Publication

2011 RESNA_ICTA Conference, Toronto, ON, Canada, June 5-8, 2011


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.


Published version. Published as part of the proceedings of the conference, 2011 RESNA_ICTA Conference, 2011. Publisher Link. © 2011 RESNA (Rehabilitation Engineering and Assistive Technology Society of North America). Used with permission.