@Article{10798587.2017.1342400,
AUTHOR = {Juan C. Quiroz, Amit Banerjee, Sergiu M. Dascalu, Sian Lun Lau},
TITLE = {Feature Selection for Activity Recognition from Smartphone Accelerometer Data},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {4},
PAGES = {785--793},
URL = {http://www.techscience.com/iasc/v24n4/39805},
ISSN = {2326-005X},
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.},
DOI = {10.1080/10798587.2017.1342400}
}