Document Type
Conference Proceeding
Language
eng
Publication Date
2011
Source Publication
2011 RESNA_ICTA Conference, Toronto, ON, Canada, June 5-8, 2011
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.
Recommended Citation
Khan, Mridul; Ahamed, Sheikh Iqbal; Rahman, Miftahur; and Smith, Roger O., "Feature Extraction Method for Real Time Human Activity Recognition on Cell Phones" (2011). Mathematics, Statistics and Computer Science Faculty Research and Publications. 183.
https://epublications.marquette.edu/mscs_fac/183
Comments
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.