Online sequential extreme learning machine algorithm based human activity recognition using inertial data

Al Jeroudi, Yazan and Ali, M. A. and Latief, Marsad and Akmeliawati, Rini (2015) Online sequential extreme learning machine algorithm based human activity recognition using inertial data. In: 2015 10th Asian Control Conference (ASCC 2015), 31st May- 3rd June 2015, Kota Kinabalu, Sabah.

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Abstract

Human activity recognition (HAR) is the basis for many real world applications concerning health care, sports and gaming industry. Different methodological perspectives have been proposed to perform HAR. One appealing methodology is to take an advantage of data that are collected from inertial sensors which are embedded in the individual's smartphone. These data contain rich amount of information about daily activities of the user. However, there is no straightforward analytical mapping between a performed activity and its corresponding data. Besides, online training for the classification in these types of applications is a concern. This paper aims at classifying human activities based on the inertial data collected from a user's smartphone. An Online Sequential Extreme Learning Machine (OSELM) method is implemented to train a single hidden layer feed-forward network (SLFN). Experimental results with an average accuracy of 82.05% are achieved.

Item Type:	Conference or Workshop Item (Invited Papers)
Additional Information:	5806/44861
Uncontrolled Keywords:	extreme learning machine; human activity recognition; online multi-classification; inertial sensing; pattern recognition;
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Prof. Dr. Rini Akmeliawati
Date Deposited:	02 Oct 2015 15:09
Last Modified:	24 May 2016 09:54
URI:	http://irep.iium.edu.my/id/eprint/44861

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