%0 Journal Article
%@ 14248220
%A Naqvi, S.F.
%A Ali, S.S.A.
%A Yahya, N.
%A Yasin, M.A.
%A Hafeez, Y.
%A Subhani, A.R.
%A Adil, S.H.
%A Saggaf, U.M.A.
%A Moinuddin, M.
%D 2020
%F scholars:12828
%I MDPI AG
%J Sensors (Switzerland)
%K 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
%N 16
%P 1-17
%R 10.3390/s20164400
%T Real-time stress assessment using sliding window based convolutional neural network
%U https://khub.utp.edu.my/scholars/12828/
%V 20
%X 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.
%Z cited By 9