TY  - JOUR
A1  - Naqvi, S.F.
A1  - Ali, S.S.A.
A1  - Yahya, N.
A1  - Yasin, M.A.
A1  - Hafeez, Y.
A1  - Subhani, A.R.
A1  - Adil, S.H.
A1  - Saggaf, U.M.A.
A1  - Moinuddin, M.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089240804&doi=10.3390%2fs20164400&partnerID=40&md5=5d5ff503da4a2e79886a5ffb96d6d2d7
N1  - cited By 9
Y1  - 2020///
VL  - 20
JF  - Sensors (Switzerland)
EP  - 17
ID  - scholars12828
IS  - 16
KW  - Computer aided diagnosis; Computer aided instruction; Convolution; Convolutional neural networks
KW  -  Computer aided diagnosis systems; Mental stress; Off-line processing; Real-time application; Reasonable accuracy; Sliding window-based; State of the art; Stress assessment
KW  -  Real time systems
KW  -  article; controlled study; convolutional neural network; diagnostic test accuracy study; feature extraction; human; human experiment; mental stress; sensitivity and specificity; stress assessment
TI  - Real-time stress assessment using sliding window based convolutional neural network
N2  - 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.
SN  - 14248220
AV  - none
PB  - MDPI AG
SP  - 1
ER  -