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Real-time stress assessment using sliding window based convolutional neural network

Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-ai...

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Main Authors: Naqvi, S.F., Ali, S.S.A., Yahya, N., Yasin, M.A., Hafeez, Y., Subhani, A.R., Adil, S.H., Saggaf, U.M.A., Moinuddin, M.
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Published: MDPI AG 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089240804&doi=10.3390%2fs20164400&partnerID=40&md5=5d5ff503da4a2e79886a5ffb96d6d2d7
http://eprints.utp.edu.my/30059/
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spelling my.utp.eprints.300592022-03-25T03:22:23Z Real-time stress assessment using sliding window based convolutional neural network Naqvi, S.F. Ali, S.S.A. Yahya, N. Yasin, M.A. Hafeez, Y. Subhani, A.R. Adil, S.H. Saggaf, U.M.A. Moinuddin, M. Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96, the sensitivity of 95, and specificity of 97. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. MDPI AG 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089240804&doi=10.3390%2fs20164400&partnerID=40&md5=5d5ff503da4a2e79886a5ffb96d6d2d7 Naqvi, S.F. and Ali, S.S.A. and Yahya, N. and Yasin, M.A. and Hafeez, Y. and Subhani, A.R. and Adil, S.H. and Saggaf, U.M.A. and Moinuddin, M. (2020) Real-time stress assessment using sliding window based convolutional neural network. Sensors (Switzerland), 20 (16). pp. 1-17. http://eprints.utp.edu.my/30059/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96, the sensitivity of 95, and specificity of 97. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
format Article
author Naqvi, S.F.
Ali, S.S.A.
Yahya, N.
Yasin, M.A.
Hafeez, Y.
Subhani, A.R.
Adil, S.H.
Saggaf, U.M.A.
Moinuddin, M.
spellingShingle Naqvi, S.F.
Ali, S.S.A.
Yahya, N.
Yasin, M.A.
Hafeez, Y.
Subhani, A.R.
Adil, S.H.
Saggaf, U.M.A.
Moinuddin, M.
Real-time stress assessment using sliding window based convolutional neural network
author_facet Naqvi, S.F.
Ali, S.S.A.
Yahya, N.
Yasin, M.A.
Hafeez, Y.
Subhani, A.R.
Adil, S.H.
Saggaf, U.M.A.
Moinuddin, M.
author_sort Naqvi, S.F.
title Real-time stress assessment using sliding window based convolutional neural network
title_short Real-time stress assessment using sliding window based convolutional neural network
title_full Real-time stress assessment using sliding window based convolutional neural network
title_fullStr Real-time stress assessment using sliding window based convolutional neural network
title_full_unstemmed Real-time stress assessment using sliding window based convolutional neural network
title_sort real-time stress assessment using sliding window based convolutional neural network
publisher MDPI AG
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089240804&doi=10.3390%2fs20164400&partnerID=40&md5=5d5ff503da4a2e79886a5ffb96d6d2d7
http://eprints.utp.edu.my/30059/
_version_ 1738657054084562944
score 13.211869

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