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Improving Accelerometer-Based Activity Recognition by Using Ensemble of Classifiers

By Tahani Daghistani and Riyad Alshammari

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 %

Topics: Activity Recognition, Sensors, Smart phones, accelerometer data, Data mining, Ensemble, Electronic computers. Computer science, QA75.5-76.95, Instruments and machines, QA71-90, Mathematics, QA1-939, Science, Q
Publisher: The Science and Information (SAI) Organization
Year: 2016
OAI identifier: oai:doaj.org/article:a98e8d25122a411aa021ee5fef2a3450
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