eprintid: 12828
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/28/28
datestamp: 2023-11-10 03:27:23
lastmod: 2023-11-10 03:27:23
status_changed: 2023-11-10 01:49:38
type: article
metadata_visibility: show
creators_name: Naqvi, S.F.
creators_name: Ali, S.S.A.
creators_name: Yahya, N.
creators_name: Yasin, M.A.
creators_name: Hafeez, Y.
creators_name: Subhani, A.R.
creators_name: Adil, S.H.
creators_name: Saggaf, U.M.A.
creators_name: Moinuddin, M.
title: Real-time stress assessment using sliding window based convolutional neural network
ispublished: pub
keywords: Computer aided diagnosis; Computer aided instruction; Convolution; Convolutional neural networks, Computer aided diagnosis systems; Mental stress; Off-line processing; Real-time application; Reasonable accuracy; Sliding window-based; State of the art; Stress assessment, Real time systems, article; controlled study; convolutional neural network; diagnostic test accuracy study; feature extraction; human; human experiment; mental stress; sensitivity and specificity; stress assessment
note: cited By 9
abstract: 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.
date: 2020
publisher: MDPI AG
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089240804&doi=10.3390%2fs20164400&partnerID=40&md5=5d5ff503da4a2e79886a5ffb96d6d2d7
id_number: 10.3390/s20164400
full_text_status: none
publication: Sensors (Switzerland)
volume: 20
number: 16
pagerange: 1-17
refereed: TRUE
issn: 14248220
citation:   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