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