Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment
Cognitive assessment plays a vital role in clinical care and research fields related to cognitive aging and cognitive health. Lately, researchers have worked towards providing resolutions to measure individual cognitive health; however, it is still difficult to use those resolutions from the real wo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/7/554 |
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| author | Mohammed Alonazi Haya Mesfer Alshahrani Fadoua Kouki Nabil Sharaf Almalki Ahmed Mahmud Jihen Majdoubi |
| author_facet | Mohammed Alonazi Haya Mesfer Alshahrani Fadoua Kouki Nabil Sharaf Almalki Ahmed Mahmud Jihen Majdoubi |
| author_sort | Mohammed Alonazi |
| collection | DOAJ |
| description | Cognitive assessment plays a vital role in clinical care and research fields related to cognitive aging and cognitive health. Lately, researchers have worked towards providing resolutions to measure individual cognitive health; however, it is still difficult to use those resolutions from the real world, and therefore using deep neural networks to evaluate cognitive health is becoming a hot research topic. Deep learning and human activity recognition are two domains that have received attention for the past few years. The former is for its relevance in application fields like health monitoring or ambient assisted living, and the latter is due to their excellent performance and recent achievements in various fields of application, namely, speech and image recognition. This research develops a novel Symbiotic Organism Search with a Deep Convolutional Neural Network-based Human Activity Recognition (SOSDCNN-HAR) model for Cognitive Health Assessment. The goal of the SOSDCNN-HAR model is to recognize human activities in an end-to-end way. For the noise elimination process, the presented SOSDCNN-HAR model involves the Wiener filtering (WF) technique. In addition, the presented SOSDCNN-HAR model follows a RetinaNet-based feature extractor for automated extraction of features. Moreover, the SOS procedure is exploited as a hyperparameter optimizing tool to enhance recognition efficiency. Furthermore, a gated recurrent unit (GRU) prototype can be employed as a categorizer to allot proper class labels. The performance validation of the SOSDCNN-HAR prototype is examined using a set of benchmark datasets. A far-reaching experimental examination reported the betterment of the SOSDCNN-HAR prototype over current approaches with enhanced precision of 86.51% and 89.50% on Penn Action and NW-UCLA datasets, respectively. |
| first_indexed | 2024-03-09T16:59:43Z |
| format | Article |
| id | doaj.art-c43b863fc1ef4b72ad19f30503fda1dc |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-09T16:59:43Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-c43b863fc1ef4b72ad19f30503fda1dc2023-11-24T14:31:44ZengMDPI AGBiomimetics2313-76732023-11-018755410.3390/biomimetics8070554Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health AssessmentMohammed Alonazi0Haya Mesfer Alshahrani1Fadoua Kouki2Nabil Sharaf Almalki3Ahmed Mahmud4Jihen Majdoubi5Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi ArabiaDepartment of Financial and Banking Sciences, Applied College at Muhail Aseer, King Khalid University, Abha 62529, Saudi ArabiaDepartment of Special Education, College of Education, King Saud University, Riyadh 12372, Saudi ArabiaResearch Center, Future University in Egypt, New Cairo 11835, EgyptDepartment of Computer Science, College of Science and Humanities at Alghat, Majmaah University, Al-Majmaah 11952, Saudi ArabiaCognitive assessment plays a vital role in clinical care and research fields related to cognitive aging and cognitive health. Lately, researchers have worked towards providing resolutions to measure individual cognitive health; however, it is still difficult to use those resolutions from the real world, and therefore using deep neural networks to evaluate cognitive health is becoming a hot research topic. Deep learning and human activity recognition are two domains that have received attention for the past few years. The former is for its relevance in application fields like health monitoring or ambient assisted living, and the latter is due to their excellent performance and recent achievements in various fields of application, namely, speech and image recognition. This research develops a novel Symbiotic Organism Search with a Deep Convolutional Neural Network-based Human Activity Recognition (SOSDCNN-HAR) model for Cognitive Health Assessment. The goal of the SOSDCNN-HAR model is to recognize human activities in an end-to-end way. For the noise elimination process, the presented SOSDCNN-HAR model involves the Wiener filtering (WF) technique. In addition, the presented SOSDCNN-HAR model follows a RetinaNet-based feature extractor for automated extraction of features. Moreover, the SOS procedure is exploited as a hyperparameter optimizing tool to enhance recognition efficiency. Furthermore, a gated recurrent unit (GRU) prototype can be employed as a categorizer to allot proper class labels. The performance validation of the SOSDCNN-HAR prototype is examined using a set of benchmark datasets. A far-reaching experimental examination reported the betterment of the SOSDCNN-HAR prototype over current approaches with enhanced precision of 86.51% and 89.50% on Penn Action and NW-UCLA datasets, respectively.https://www.mdpi.com/2313-7673/8/7/554human activity recognitioncognitive health assessmentdeep neural networkshyperparameter tuningdeep convolutional neural networkmetaheuristics |
| spellingShingle | Mohammed Alonazi Haya Mesfer Alshahrani Fadoua Kouki Nabil Sharaf Almalki Ahmed Mahmud Jihen Majdoubi Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment Biomimetics human activity recognition cognitive health assessment deep neural networks hyperparameter tuning deep convolutional neural network metaheuristics |
| title | Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment |
| title_full | Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment |
| title_fullStr | Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment |
| title_full_unstemmed | Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment |
| title_short | Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment |
| title_sort | deep convolutional neural network with symbiotic organism search based human activity recognition for cognitive health assessment |
| topic | human activity recognition cognitive health assessment deep neural networks hyperparameter tuning deep convolutional neural network metaheuristics |
| url | https://www.mdpi.com/2313-7673/8/7/554 |
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