relation: https://khub.utp.edu.my/scholars/12828/
title: Real-time stress assessment using sliding window based convolutional neural network
creator: Naqvi, S.F.
creator: Ali, S.S.A.
creator: Yahya, N.
creator: Yasin, M.A.
creator: Hafeez, Y.
creator: Subhani, A.R.
creator: Adil, S.H.
creator: Saggaf, U.M.A.
creator: Moinuddin, M.
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.
publisher: MDPI AG
date: 2020
type: Article
type: PeerReviewed
identifier:   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.  ISSN 14248220     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089240804&doi=10.3390%2fs20164400&partnerID=40&md5=5d5ff503da4a2e79886a5ffb96d6d2d7
relation: 10.3390/s20164400
identifier: 10.3390/s20164400