2021
DOI: 10.3390/e23081065
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A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors

Abstract: Human activity recognition (HAR) plays a vital role in different real-world applications such as in tracking elderly activities for elderly care services, in assisted living environments, smart home interactions, healthcare monitoring applications, electronic games, and various human–computer interaction (HCI) applications, and is an essential part of the Internet of Healthcare Things (IoHT) services. However, the high dimensionality of the collected data from these applications has the largest influence on th… Show more

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Cited by 53 publications
(28 citation statements)
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References 61 publications
(74 reference statements)
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“…Helmi et al [38] proposed a lightweight feature selection (FS) technique to improve classification for a real-world human activity recognition problem. The new FS approach, known as GBOGWO, intends to increase the performance of the gradient-based optimizer (GBO) algorithm by utilizing grey wolf optimizer's (GWO) operators.…”
Section: Related Workmentioning
confidence: 99%
“…Helmi et al [38] proposed a lightweight feature selection (FS) technique to improve classification for a real-world human activity recognition problem. The new FS approach, known as GBOGWO, intends to increase the performance of the gradient-based optimizer (GBO) algorithm by utilizing grey wolf optimizer's (GWO) operators.…”
Section: Related Workmentioning
confidence: 99%
“…Feature selection can address this problem, through which a subset of the relevant and effective features must be found. Feature selection is used in a variety of real-world applications such as disease diagnosis [4,5], email spam detection [6], text clustering [7,8], and human activity recognition [9].…”
Section: Introductionmentioning
confidence: 99%
“…During past decades, optimization techniques have been developed widely to solve complex problems that emerged in different fields of science, such as engineering [1][2][3][4][5][6][7][8][9], clustering [10][11][12][13][14][15][16][17][18], feature selection [19][20][21][22][23][24][25][26][27][28], and task scheduling [29][30][31][32]. Such optimization problems mainly involve characteristics such as linear/non-linear constraints, nondifferentiable functions, and a substantial number of decision variables.…”
Section: Introductionmentioning
confidence: 99%