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

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 senso...

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Main Authors:	Al Jeroudi, Yazan, Ali, M. A., Latief, Marsad, Akmeliawati, Rini
Format:	Conference or Workshop Item
Language:	English English
Published:	IEEE 2015
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/44861/ http://irep.iium.edu.my/44861/ http://irep.iium.edu.my/44861/ http://irep.iium.edu.my/44861/4/44861-Online_sequential_extreme_learning_machine_algorithm_based_human_activity_recognition_using_inertial_data_Full_article.pdf http://irep.iium.edu.my/44861/7/ASCC-organizer.pdf

id	iium-44861
recordtype	eprints
spelling	iium-448612016-05-24T01:54:31Z http://irep.iium.edu.my/44861/ Online sequential extreme learning machine algorithm based human activity recognition using inertial data Al Jeroudi, Yazan Ali, M. A. Latief, Marsad Akmeliawati, Rini TK Electrical engineering. Electronics Nuclear engineering 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. IEEE 2015 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/44861/4/44861-Online_sequential_extreme_learning_machine_algorithm_based_human_activity_recognition_using_inertial_data_Full_article.pdf application/pdf en http://irep.iium.edu.my/44861/7/ASCC-organizer.pdf 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. http://dx.doi.org/10.1109/ASCC.2015.7244597 10.1109/ASCC.2015.7244597
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Al Jeroudi, Yazan Ali, M. A. Latief, Marsad Akmeliawati, Rini Online sequential extreme learning machine algorithm based human activity recognition using inertial data
description	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.
format	Conference or Workshop Item
author	Al Jeroudi, Yazan Ali, M. A. Latief, Marsad Akmeliawati, Rini
author_facet	Al Jeroudi, Yazan Ali, M. A. Latief, Marsad Akmeliawati, Rini
author_sort	Al Jeroudi, Yazan
title	Online sequential extreme learning machine algorithm based human activity recognition using inertial data
title_short	Online sequential extreme learning machine algorithm based human activity recognition using inertial data
title_full	Online sequential extreme learning machine algorithm based human activity recognition using inertial data
title_fullStr	Online sequential extreme learning machine algorithm based human activity recognition using inertial data
title_full_unstemmed	Online sequential extreme learning machine algorithm based human activity recognition using inertial data
title_sort	online sequential extreme learning machine algorithm based human activity recognition using inertial data
publisher	IEEE
publishDate	2015
url	http://irep.iium.edu.my/44861/ http://irep.iium.edu.my/44861/ http://irep.iium.edu.my/44861/ http://irep.iium.edu.my/44861/4/44861-Online_sequential_extreme_learning_machine_algorithm_based_human_activity_recognition_using_inertial_data_Full_article.pdf http://irep.iium.edu.my/44861/7/ASCC-organizer.pdf
first_indexed	2023-09-18T21:03:47Z
last_indexed	2023-09-18T21:03:47Z
_version_	1777410798155464704

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

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