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 More >
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