Hybrid feature selection technique for classification of human activity recognition

Through the advancement of wearable sensors, wireless communication, and machine learning techniques, Assistive Technologies (AT) which endorse autonomous, active, and healthy lifestyles are emerging in recent years. Among these advances, Human Activity Recognition (HAR) is one of the most innovativ...

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Main Author:	Yusup, Norfadzlan
Format:	Thesis
Language:	English
Published:	2021
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/101548/1/NorfadzlanYusupPSC2021.pdf
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id	my-utm-ep.101548
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spelling	my-utm-ep.1015482023-06-26T02:02:40Z Hybrid feature selection technique for classification of human activity recognition 2021 Yusup, Norfadzlan QA75 Electronic computers. Computer science Through the advancement of wearable sensors, wireless communication, and machine learning techniques, Assistive Technologies (AT) which endorse autonomous, active, and healthy lifestyles are emerging in recent years. Among these advances, Human Activity Recognition (HAR) is one of the most innovative means to support or monitor older people in their daily lives. Extensive research in the field of HAR is also essential for the enhancement of the quality of a person"s health. However, misclassifications such as intra-class variation and inter-class overlap in similar activities degrade classification accuracy. To improve the recognition of daily human activities, handcrafted features of time-domain and frequency-domain are combined. However, several extracted features may not be significant in describing the activities. Therefore, this research aims to propose a hybrid feature selection technique for optimal human activities recognition. The methodology proposed for this research is the Ensemble Filter (Relief-F and mRMR) to select the most relevant and less redundant features. Although a filter feature ranking approach is commonly used in related studies, most works fail to consider the threshold limit to exclude unnecessary and redundant features. The hybrid Binary Harmony Search-Artificial Bee Colony (BHS-ABC) algorithm further evaluated the quality of the selected features in this research. Two HAR datasets using accelerometer and gyroscope sensors from the smartphone device were evaluated, covering various daily human activities. An ensemble Random Forest (RF) was used as the base classifier to evaluate the performance of the hybrid algorithm. The performance was then compared with other shallow machine learning techniques such as Support Vector Machine (SVM), k-Nearest Neighbour (kNN), and Naïve Bayes (NB). One-versus-All (OvA) class binarization technique was adopted to enhance the detection of high-class correlations by converting initial problems of multiple classifications into a set of two-class classification problems. The proposed hybrid of Ensemble Filter with BHS-ABC for this research is capable of obtaining optimum feature subsets with high accuracy. The average of SBHAR and USCHAD datasets accuracy have improved by 3.2% and 1.6%, respectively, from the benchmark results. 2021 Thesis http://eprints.utm.my/id/eprint/101548/ http://eprints.utm.my/id/eprint/101548/1/NorfadzlanYusupPSC2021.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150588 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Yusup, Norfadzlan Hybrid feature selection technique for classification of human activity recognition
description	Through the advancement of wearable sensors, wireless communication, and machine learning techniques, Assistive Technologies (AT) which endorse autonomous, active, and healthy lifestyles are emerging in recent years. Among these advances, Human Activity Recognition (HAR) is one of the most innovative means to support or monitor older people in their daily lives. Extensive research in the field of HAR is also essential for the enhancement of the quality of a person"s health. However, misclassifications such as intra-class variation and inter-class overlap in similar activities degrade classification accuracy. To improve the recognition of daily human activities, handcrafted features of time-domain and frequency-domain are combined. However, several extracted features may not be significant in describing the activities. Therefore, this research aims to propose a hybrid feature selection technique for optimal human activities recognition. The methodology proposed for this research is the Ensemble Filter (Relief-F and mRMR) to select the most relevant and less redundant features. Although a filter feature ranking approach is commonly used in related studies, most works fail to consider the threshold limit to exclude unnecessary and redundant features. The hybrid Binary Harmony Search-Artificial Bee Colony (BHS-ABC) algorithm further evaluated the quality of the selected features in this research. Two HAR datasets using accelerometer and gyroscope sensors from the smartphone device were evaluated, covering various daily human activities. An ensemble Random Forest (RF) was used as the base classifier to evaluate the performance of the hybrid algorithm. The performance was then compared with other shallow machine learning techniques such as Support Vector Machine (SVM), k-Nearest Neighbour (kNN), and Naïve Bayes (NB). One-versus-All (OvA) class binarization technique was adopted to enhance the detection of high-class correlations by converting initial problems of multiple classifications into a set of two-class classification problems. The proposed hybrid of Ensemble Filter with BHS-ABC for this research is capable of obtaining optimum feature subsets with high accuracy. The average of SBHAR and USCHAD datasets accuracy have improved by 3.2% and 1.6%, respectively, from the benchmark results.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Yusup, Norfadzlan
author_facet	Yusup, Norfadzlan
author_sort	Yusup, Norfadzlan
title	Hybrid feature selection technique for classification of human activity recognition
title_short	Hybrid feature selection technique for classification of human activity recognition
title_full	Hybrid feature selection technique for classification of human activity recognition
title_fullStr	Hybrid feature selection technique for classification of human activity recognition
title_full_unstemmed	Hybrid feature selection technique for classification of human activity recognition
title_sort	hybrid feature selection technique for classification of human activity recognition
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Computing
publishDate	2021
url	http://eprints.utm.my/id/eprint/101548/1/NorfadzlanYusupPSC2021.pdf
_version_	1776100724238712832

Hybrid feature selection technique for classification of human activity recognition

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