A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition

Nowadays, many mobile phones have been equipped with sensors to enable the delivery of advanced eatures/services to the users. Accelerometer is one of the sensors that embedded to several types of mobile phones. Our earlier research has shown that data from mobile-phone embedded accelerometer can be...

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Main Authors:	Ayu, Media Anugerah, Ismail, Siti Aisyah, Abdul Matin, Ahmad Faridi, Mantoro, Teddy
Format:	Article
Language:	English
Published:	Elsevier 2012
Subjects:	T Technology (General) TK5101 Telecommunication. Including telegraphy, radio, radar, television
Online Access:	http://irep.iium.edu.my/25609/ http://irep.iium.edu.my/25609/ http://irep.iium.edu.my/25609/ http://irep.iium.edu.my/25609/4/ProcediaEng_1-s2.0-S1877705812025520-main.pdf

id	iium-25609
recordtype	eprints
spelling	iium-256092013-06-07T02:34:47Z http://irep.iium.edu.my/25609/ A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition Ayu, Media Anugerah Ismail, Siti Aisyah Abdul Matin, Ahmad Faridi Mantoro, Teddy T Technology (General) TK5101 Telecommunication. Including telegraphy, radio, radar, television Nowadays, many mobile phones have been equipped with sensors to enable the delivery of advanced eatures/services to the users. Accelerometer is one of the sensors that embedded to several types of mobile phones. Our earlier research has shown that data from mobile-phone embedded accelerometer can be used for activity recognition purpose [1]. As a continuation of the research towards the search for a suitable and reliable algorithm for real-time activity recognition using mobile phone, an evaluation and comparison study of the performance of seven different categories of classifier algorithms in classifying user activities were conducted. Five basic human activities (jogging, jumping, sitting, standing, and walking) were tested. The training and testing data were done using Weka 3.6.6 data mining tool. The overall accuracy rate for classifier training managed to exceed 96% and exceeded 90% for classifier testing, which are very encouraging results. Elsevier 2012 Article PeerReviewed application/pdf en http://irep.iium.edu.my/25609/4/ProcediaEng_1-s2.0-S1877705812025520-main.pdf Ayu, Media Anugerah and Ismail, Siti Aisyah and Abdul Matin, Ahmad Faridi and Mantoro, Teddy (2012) A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition. Procedia Engineering, 41. pp. 224-229. ISSN 1877-7058 http://www.elsevier.com/wps/find/journaldescription.cws_home/719240/description 10.1016/j.proeng.2012.07.166
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General) TK5101 Telecommunication. Including telegraphy, radio, radar, television
spellingShingle	T Technology (General) TK5101 Telecommunication. Including telegraphy, radio, radar, television Ayu, Media Anugerah Ismail, Siti Aisyah Abdul Matin, Ahmad Faridi Mantoro, Teddy A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition
description	Nowadays, many mobile phones have been equipped with sensors to enable the delivery of advanced eatures/services to the users. Accelerometer is one of the sensors that embedded to several types of mobile phones. Our earlier research has shown that data from mobile-phone embedded accelerometer can be used for activity recognition purpose [1]. As a continuation of the research towards the search for a suitable and reliable algorithm for real-time activity recognition using mobile phone, an evaluation and comparison study of the performance of seven different categories of classifier algorithms in classifying user activities were conducted. Five basic human activities (jogging, jumping, sitting, standing, and walking) were tested. The training and testing data were done using Weka 3.6.6 data mining tool. The overall accuracy rate for classifier training managed to exceed 96% and exceeded 90% for classifier testing, which are very encouraging results.
format	Article
author	Ayu, Media Anugerah Ismail, Siti Aisyah Abdul Matin, Ahmad Faridi Mantoro, Teddy
author_facet	Ayu, Media Anugerah Ismail, Siti Aisyah Abdul Matin, Ahmad Faridi Mantoro, Teddy
author_sort	Ayu, Media Anugerah
title	A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition
title_short	A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition
title_full	A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition
title_fullStr	A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition
title_full_unstemmed	A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition
title_sort	comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition
publisher	Elsevier
publishDate	2012
url	http://irep.iium.edu.my/25609/ http://irep.iium.edu.my/25609/ http://irep.iium.edu.my/25609/ http://irep.iium.edu.my/25609/4/ProcediaEng_1-s2.0-S1877705812025520-main.pdf
first_indexed	2023-09-18T20:38:10Z
last_indexed	2023-09-18T20:38:10Z
_version_	1777409185965670400

A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition

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