A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors
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 intera...
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| Format: | Article |
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MDPI AG
2021-08-01
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| Online Access: | https://www.mdpi.com/1099-4300/23/8/1065 |
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| author | Ahmed Mohamed Helmi Mohammed A. A. Al-qaness Abdelghani Dahou Robertas Damaševičius Tomas Krilavičius Mohamed Abd Elaziz |
| author_facet | Ahmed Mohamed Helmi Mohammed A. A. Al-qaness Abdelghani Dahou Robertas Damaševičius Tomas Krilavičius Mohamed Abd Elaziz |
| author_sort | Ahmed Mohamed Helmi |
| collection | DOAJ |
| description | 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 the quality of the HAR model. Therefore, in this paper, we propose an efficient HAR system using a lightweight feature selection (FS) method to enhance the HAR classification process. The developed FS method, called GBOGWO, aims to improve the performance of the Gradient-based optimizer (GBO) algorithm by using the operators of the grey wolf optimizer (GWO). First, GBOGWO is used to select the appropriate features; then, the support vector machine (SVM) is used to classify the activities. To assess the performance of GBOGWO, extensive experiments using well-known UCI-HAR and WISDM datasets were conducted. Overall outcomes show that GBOGWO improved the classification accuracy with an average accuracy of 98%. |
| first_indexed | 2024-03-10T08:49:30Z |
| format | Article |
| id | doaj.art-2a264037b54d483ab1d29de8e1cb7aa4 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T08:49:30Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-2a264037b54d483ab1d29de8e1cb7aa42023-11-22T07:35:47ZengMDPI AGEntropy1099-43002021-08-01238106510.3390/e23081065A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone SensorsAhmed Mohamed Helmi0Mohammed A. A. Al-qaness1Abdelghani Dahou2Robertas Damaševičius3Tomas Krilavičius 4Mohamed Abd Elaziz5Department of Computer and Systems Engineering, Faculty of Engineering, Zagazig University, Zagazig 44519, EgyptState Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaLDDI Laboratory, Faculty of Science and Technology, University of Ahmed DRAIA, Adrar 01000, AlgeriaDepartment of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, LithuaniaDepartment of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, LithuaniaDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, EgyptHuman 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 the quality of the HAR model. Therefore, in this paper, we propose an efficient HAR system using a lightweight feature selection (FS) method to enhance the HAR classification process. The developed FS method, called GBOGWO, aims to improve the performance of the Gradient-based optimizer (GBO) algorithm by using the operators of the grey wolf optimizer (GWO). First, GBOGWO is used to select the appropriate features; then, the support vector machine (SVM) is used to classify the activities. To assess the performance of GBOGWO, extensive experiments using well-known UCI-HAR and WISDM datasets were conducted. Overall outcomes show that GBOGWO improved the classification accuracy with an average accuracy of 98%.https://www.mdpi.com/1099-4300/23/8/1065human activity recognitionfeature selectiongradient-based optimizergrey wolf optimizermetaheuristic |
| spellingShingle | Ahmed Mohamed Helmi Mohammed A. A. Al-qaness Abdelghani Dahou Robertas Damaševičius Tomas Krilavičius Mohamed Abd Elaziz A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors Entropy human activity recognition feature selection gradient-based optimizer grey wolf optimizer metaheuristic |
| title | A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors |
| title_full | A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors |
| title_fullStr | A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors |
| title_full_unstemmed | A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors |
| title_short | A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors |
| title_sort | novel hybrid gradient based optimizer and grey wolf optimizer feature selection method for human activity recognition using smartphone sensors |
| topic | human activity recognition feature selection gradient-based optimizer grey wolf optimizer metaheuristic |
| url | https://www.mdpi.com/1099-4300/23/8/1065 |
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