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

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 accur...

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Main Authors:	AlDahoul, Nouar, Htike, Zaw Zaw
Format:	Article
Language:	English English English
Published:	2018
Subjects:	Q350 Information theory
Online Access:	http://irep.iium.edu.my/69665/ http://irep.iium.edu.my/69665/ http://irep.iium.edu.my/69665/1/Fulpaper.pdf http://irep.iium.edu.my/69665/13/Acceptance%20Letter%2069665.pdf http://irep.iium.edu.my/69665/19/69665_Feature%20Fusion%20H-ELM%20based%20learned%20features%20and%20hand-crafted%20features_scopus.pdf

id	iium-69665
recordtype	eprints
spelling	iium-696652019-08-20T06:42:03Z http://irep.iium.edu.my/69665/ Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition AlDahoul, Nouar Htike, Zaw Zaw Q350 Information theory 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). 2018-12 Article PeerReviewed application/pdf en http://irep.iium.edu.my/69665/1/Fulpaper.pdf application/pdf en http://irep.iium.edu.my/69665/13/Acceptance%20Letter%2069665.pdf application/pdf en http://irep.iium.edu.my/69665/19/69665_Feature%20Fusion%20H-ELM%20based%20learned%20features%20and%20hand-crafted%20features_scopus.pdf 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) http://thesai.org/Publications/IJACSA
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	Q350 Information theory
spellingShingle	Q350 Information theory AlDahoul, Nouar Htike, Zaw Zaw Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition
description	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).
format	Article
author	AlDahoul, Nouar Htike, Zaw Zaw
author_facet	AlDahoul, Nouar Htike, Zaw Zaw
author_sort	AlDahoul, Nouar
title	Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition
title_short	Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition
title_full	Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition
title_fullStr	Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition
title_full_unstemmed	Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition
title_sort	feature fusion h-elm based learned features and hand-crafted features for human activity recognition
publishDate	2018
url	http://irep.iium.edu.my/69665/ http://irep.iium.edu.my/69665/ http://irep.iium.edu.my/69665/1/Fulpaper.pdf http://irep.iium.edu.my/69665/13/Acceptance%20Letter%2069665.pdf http://irep.iium.edu.my/69665/19/69665_Feature%20Fusion%20H-ELM%20based%20learned%20features%20and%20hand-crafted%20features_scopus.pdf
first_indexed	2023-09-18T21:38:53Z
last_indexed	2023-09-18T21:38:53Z
_version_	1777413005719371776

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

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