Open Access
ARTICLE
Feature Selection for Activity Recognition from Smartphone Accelerometer Data
Juan C. Quiroza, Amit Banerjeeb, Sergiu M. Dascaluc, Sian Lun Laua
a Department of Computing and Information Systems, Sunway University, Bandar Sunway, Malaysia;
b School of Science, Engineering and Technology, Penn State Harrisburg, Middletown, USA;
c Department of Computer Science and Engineering, University of Nevada, Reno, USA
* Corresponding Author: Juan C. Quiroz,
Intelligent Automation & Soft Computing 2018, 24(4), 785-793. https://doi.org/10.1080/10798587.2017.1342400
Abstract
We use the public Human Activity Recognition Using Smartphones (HARUS) data-set to investigate
and identify the most informative features for determining the physical activity performed by a user
based on smartphone accelerometer and gyroscope data. The HARUS data-set includes 561 time
domain and frequency domain features extracted from sensor readings collected from a smartphone
carried by 30 users while performing specific activities. We compare the performance of a decision
tree, support vector machines, Naive Bayes, multilayer perceptron, and bagging. We report the various
classification performances of these algorithms for subject independent cases. Our results show that
bagging and the multilayer perceptron achieve the highest classification accuracies across all feature
sets. In addition, the signal from gravity contains the most information for classification of activities in
the HARUS data-set.
Keywords
Cite This Article
J. C. Quiroz, . Amit Banerjee, S. M. Dascalu and S. Lun Lau, "Feature selection for activity recognition from smartphone accelerometer data,"
Intelligent Automation & Soft Computing, vol. 24, no.4, pp. 785–793, 2018. https://doi.org/10.1080/10798587.2017.1342400