Activity Recognition Based On Accelerometer Sensor Using Combinational Classifiers

Muhammad Noorazlan Shah, Zainuddin and Md Nasir, Sulaiman and Norwati, Mustapha and Thinagaran, Perumal (2015) Activity Recognition Based On Accelerometer Sensor Using Combinational Classifiers. 2015 IEEE Confernece On Open Systems (ICOS). pp. 68-73. ISSN 978-146739434-5

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Abstract

In recent years, people nowadays easily to contact each other by using smartphone. Most of the smartphone now embedded with inertial sensors such accelerometer, gyroscope, magnetic sensors, GPS and vision sensors. Furthermore, various researchers now dealing with this kind of sensors to recognize human activities incorporate with machine learning algorithm not only in the field of medical diagnosis, forecasting, security and for better live being as well. Activity recognition using various smartphone sensors can be considered as a one of the crucial tasks that needs to be studied. In this paper, we proposed various combination classifiers models consists of J48, Multilayer Perceptron and Logistic Regression to capture the smoothest activity with higher frequency of the result using vote algorithmn. The aim of this study is to evaluate the performance of recognition the six activities using ensemble approach. Publicly accelerometer dataset obtained from Wireless Sensor Data Mining (WISDM) lab has been used in this study. The result of classification was validated using 10-fold cross validation algorithm in order to make sure all the experiments perform well.

Item Type: Article
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Electronics and Computer Engineering > Department of Computer Engineering
Depositing User: Mohd Hannif Jamaludin
Date Deposited: 02 Sep 2016 00:11
Last Modified: 02 Sep 2016 00:11
URI: http://eprints.utem.edu.my/id/eprint/17090
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