Juan C. Quiroza, Amit Banerjeeb, Sergiu M. Dascaluc, Sian Lun Laua
Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 785-793, 2018, DOI: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… More >
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