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Journal of Electrical and Computer Engineering
Volume 2015, Article ID 140820, 9 pages
Research Article

Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework

Department of Physics, Guangdong University of Education, Guangzhou 510303, China

Received 31 January 2015; Revised 17 April 2015; Accepted 19 May 2015

Academic Editor: Sos Agaian

Copyright © 2015 Yuhuang Zheng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Human activity recognition via triaxial accelerometers can provide valuable information for evaluating functional abilities. In this paper, we present an accelerometer sensor-based approach for human activity recognition. Our proposed recognition method used a hierarchical scheme, where the recognition of ten activity classes was divided into five distinct classification problems. Every classifier used the Least Squares Support Vector Machine (LS-SVM) and Naive Bayes (NB) algorithm to distinguish different activity classes. The activity class was recognized based on the mean, variance, entropy of magnitude, and angle of triaxial accelerometer signal features. Our proposed activity recognition method recognized ten activities with an average accuracy of 95.6% using only a single triaxial accelerometer.