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Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition

AlDahoul, Nouar and Htike, Zaw Zaw (2018) Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition. International Journal of Advanced Computer Science and Applications. ISSN 2158-107X E-ISSN 2156-5570 (In Press)

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Recognizing human activities is one of the main goals of human-centered intelligent systems. Smartphone sensors produce a continuous sequence of observations. These observations are noisy, unstructured and high dimensional. Therefore, efficient features have to be extracted in order to perform accurate classification. This paper proposes a combination of Hierarchical and kernel Extreme Learning Machine (HK-ELM) methods to learn features and map them to specific classes in a short time. Moreover, a feature fusion approach is proposed to combine H-ELM based learned features with hand-crafted ones. Our proposed method was found to outperform state-of-the-art in terms of accuracy and training time. It gives accuracy of 97.62 % and takes 3.4 seconds as a training time by using a normal Central Processing Unit (CPU).

Item Type: Article (Journal)
Uncontrolled Keywords: Hierarchical Extreme Learning Machine – Kernel Extreme Learning Machine – Deep Learning – Feature Learning - Human Activity Recognition – Feature Fusion
Subjects: Q Science > Q Science (General) > Q300 Cybernetics > Q350 Information theory
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Depositing User: Mr. Zaw Zaw Htike
Date Deposited: 22 Jan 2019 13:37
Last Modified: 10 Apr 2020 08:51
URI: http://irep.iium.edu.my/id/eprint/69665

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