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A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents
Sleep-deprived fatigued person is likely to commit more errors that may even prove to be fatal. Thus, it is necessary to recognize this fatigue. The novelty of the proposed research work for the detection of this fatigue is that it is nonintrusive and based on multimodal feature fusion. In the propo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10144633/ https://www.ncbi.nlm.nih.gov/pubmed/37112470 http://dx.doi.org/10.3390/s23084129 |
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author | Virk, Jitender Singh Singh, Mandeep Singh, Mandeep Panjwani, Usha Ray, Koushik |
author_facet | Virk, Jitender Singh Singh, Mandeep Singh, Mandeep Panjwani, Usha Ray, Koushik |
author_sort | Virk, Jitender Singh |
collection | PubMed |
description | Sleep-deprived fatigued person is likely to commit more errors that may even prove to be fatal. Thus, it is necessary to recognize this fatigue. The novelty of the proposed research work for the detection of this fatigue is that it is nonintrusive and based on multimodal feature fusion. In the proposed methodology, fatigue is detected by obtaining features from four domains: visual images, thermal images, keystroke dynamics, and voice features. In the proposed methodology, the samples of a volunteer (subject) are obtained from all four domains for feature extraction, and empirical weights are assigned to the four different domains. Young, healthy volunteers (n = 60) between the age group of 20 to 30 years participated in the experimental study. Further, they abstained from the consumption of alcohol, caffeine, or other drugs impacting their sleep pattern during the study. Through this multimodal technique, appropriate weights are given to the features obtained from the four domains. The results are compared with k-nearest neighbors (kNN), support vector machines (SVM), random tree, random forest, and multilayer perceptron classifiers. The proposed nonintrusive technique has obtained an average detection accuracy of 93.33% in 3-fold cross-validation. |
format | Online Article Text |
id | pubmed-10144633 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101446332023-04-29 A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents Virk, Jitender Singh Singh, Mandeep Singh, Mandeep Panjwani, Usha Ray, Koushik Sensors (Basel) Article Sleep-deprived fatigued person is likely to commit more errors that may even prove to be fatal. Thus, it is necessary to recognize this fatigue. The novelty of the proposed research work for the detection of this fatigue is that it is nonintrusive and based on multimodal feature fusion. In the proposed methodology, fatigue is detected by obtaining features from four domains: visual images, thermal images, keystroke dynamics, and voice features. In the proposed methodology, the samples of a volunteer (subject) are obtained from all four domains for feature extraction, and empirical weights are assigned to the four different domains. Young, healthy volunteers (n = 60) between the age group of 20 to 30 years participated in the experimental study. Further, they abstained from the consumption of alcohol, caffeine, or other drugs impacting their sleep pattern during the study. Through this multimodal technique, appropriate weights are given to the features obtained from the four domains. The results are compared with k-nearest neighbors (kNN), support vector machines (SVM), random tree, random forest, and multilayer perceptron classifiers. The proposed nonintrusive technique has obtained an average detection accuracy of 93.33% in 3-fold cross-validation. MDPI 2023-04-20 /pmc/articles/PMC10144633/ /pubmed/37112470 http://dx.doi.org/10.3390/s23084129 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Virk, Jitender Singh Singh, Mandeep Singh, Mandeep Panjwani, Usha Ray, Koushik A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents |
title | A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents |
title_full | A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents |
title_fullStr | A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents |
title_full_unstemmed | A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents |
title_short | A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents |
title_sort | multimodal feature fusion framework for sleep-deprived fatigue detection to prevent accidents |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10144633/ https://www.ncbi.nlm.nih.gov/pubmed/37112470 http://dx.doi.org/10.3390/s23084129 |
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