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DOI: 10.14569/IJACSA.2016.070520
PDF

Improving Accelerometer-Based Activity Recognition by Using Ensemble of Classifiers

Author 1: Tahani Daghistani
Author 2: Riyad Alshammari

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 5, 2016.

  • Abstract and Keywords
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Abstract: In line with the increasing use of sensors and health application, there are huge efforts on processing of collected data to extract valuable information such as accelerometer data. This study will propose activity recognition model aim to detect the activities by employing ensemble of classifiers techniques using the Wireless Sensor Data Mining (WISDM). The model will recognize six activities namely walking, jogging, upstairs, downstairs, sitting, and standing. Many experiments are conducted to determine the best classifier combination for activity recognition. An improvement is observed in the performance when the classifiers are combined than when used individually. An ensemble model is built using AdaBoost in combination with decision tree algorithm C4.5. The model effectively enhances the performance with an accuracy level of 94.04 %.

Keywords: Activity Recognition; Sensors; Smart phones; accelerometer data; Data mining; Ensemble

Tahani Daghistani and Riyad Alshammari, “Improving Accelerometer-Based Activity Recognition by Using Ensemble of Classifiers” International Journal of Advanced Computer Science and Applications(IJACSA), 7(5), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070520

@article{Daghistani2016,
title = {Improving Accelerometer-Based Activity Recognition by Using Ensemble of Classifiers},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070520},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070520},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {5},
author = {Tahani Daghistani and Riyad Alshammari}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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