Study of acceleration plethysmogram based biometric identification incorporating different time instances

This study investigates the effectiveness of acceleration plethysmogram (APG) to be applied as a biometric identification system in different time instances. Currently, most of the study actively discusses on the ability of photoplethysmogram (PPG) for person identification. To the best of our knowl...

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Main Authors:	Mohd Azam, Siti Nurfarah Ain, Sidek, Khairul Azami
Format:	Article
Language:	English English
Published:	American Scientific Publishers 2017
Subjects:	TK1001 Production of electric energy. Powerplants
Online Access:	http://irep.iium.edu.my/63135/ http://irep.iium.edu.my/63135/ http://irep.iium.edu.my/63135/ http://irep.iium.edu.my/63135/1/63135_Study%20of%20acceleration%20plethysmogram%20based%20biometric_article.pdf http://irep.iium.edu.my/63135/2/63135_Study%20of%20acceleration%20plethysmogram%20based%20biometric_scopus.pdf

id	iium-63135
recordtype	eprints
spelling	iium-631352018-05-07T00:42:34Z http://irep.iium.edu.my/63135/ Study of acceleration plethysmogram based biometric identification incorporating different time instances Mohd Azam, Siti Nurfarah Ain Sidek, Khairul Azami TK1001 Production of electric energy. Powerplants This study investigates the effectiveness of acceleration plethysmogram (APG) to be applied as a biometric identification system in different time instances. Currently, most of the study actively discusses on the ability of photoplethysmogram (PPG) for person identification. To the best of our knowledge, little has been said on related studies on APG signals. A total of 5 PPG signals were collected from a publicly available online repository, which is MIMIC II Waveform Database, version 3, part 3 for two different periods and then undergoes preprocessing using a low pass filter. After that, the signals were segmented and later differentiated to produce APG signals. Lastly, the APG signals were classified using four different types of classifiers, namely, Naïve Bayes, Bayes Network, Multilayer Perceptron (MLP) and Radial Basis Function (RBF). Based on the experimentation results, the accuracy for all classifiers increase when applying APG as a biometric modality of up to 11.72% as compared to PPG signals. American Scientific Publishers 2017-11 Article PeerReviewed application/pdf en http://irep.iium.edu.my/63135/1/63135_Study%20of%20acceleration%20plethysmogram%20based%20biometric_article.pdf application/pdf en http://irep.iium.edu.my/63135/2/63135_Study%20of%20acceleration%20plethysmogram%20based%20biometric_scopus.pdf Mohd Azam, Siti Nurfarah Ain and Sidek, Khairul Azami (2017) Study of acceleration plethysmogram based biometric identification incorporating different time instances. Advanced Science Letters, 23 (11). pp. 11335-11339. ISSN 1936-6612 E-ISSN 1936-7317 http://www.ingentaconnect.com/content/asp/asl/2017/00000023/00000011/art00195 10.1166/asl.2017.10278
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK1001 Production of electric energy. Powerplants
spellingShingle	TK1001 Production of electric energy. Powerplants Mohd Azam, Siti Nurfarah Ain Sidek, Khairul Azami Study of acceleration plethysmogram based biometric identification incorporating different time instances
description	This study investigates the effectiveness of acceleration plethysmogram (APG) to be applied as a biometric identification system in different time instances. Currently, most of the study actively discusses on the ability of photoplethysmogram (PPG) for person identification. To the best of our knowledge, little has been said on related studies on APG signals. A total of 5 PPG signals were collected from a publicly available online repository, which is MIMIC II Waveform Database, version 3, part 3 for two different periods and then undergoes preprocessing using a low pass filter. After that, the signals were segmented and later differentiated to produce APG signals. Lastly, the APG signals were classified using four different types of classifiers, namely, Naïve Bayes, Bayes Network, Multilayer Perceptron (MLP) and Radial Basis Function (RBF). Based on the experimentation results, the accuracy for all classifiers increase when applying APG as a biometric modality of up to 11.72% as compared to PPG signals.
format	Article
author	Mohd Azam, Siti Nurfarah Ain Sidek, Khairul Azami
author_facet	Mohd Azam, Siti Nurfarah Ain Sidek, Khairul Azami
author_sort	Mohd Azam, Siti Nurfarah Ain
title	Study of acceleration plethysmogram based biometric identification incorporating different time instances
title_short	Study of acceleration plethysmogram based biometric identification incorporating different time instances
title_full	Study of acceleration plethysmogram based biometric identification incorporating different time instances
title_fullStr	Study of acceleration plethysmogram based biometric identification incorporating different time instances
title_full_unstemmed	Study of acceleration plethysmogram based biometric identification incorporating different time instances
title_sort	study of acceleration plethysmogram based biometric identification incorporating different time instances
publisher	American Scientific Publishers
publishDate	2017
url	http://irep.iium.edu.my/63135/ http://irep.iium.edu.my/63135/ http://irep.iium.edu.my/63135/ http://irep.iium.edu.my/63135/1/63135_Study%20of%20acceleration%20plethysmogram%20based%20biometric_article.pdf http://irep.iium.edu.my/63135/2/63135_Study%20of%20acceleration%20plethysmogram%20based%20biometric_scopus.pdf
first_indexed	2023-09-18T21:29:35Z
last_indexed	2023-09-18T21:29:35Z
_version_	1777412420974673920

Study of acceleration plethysmogram based biometric identification incorporating different time instances

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