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Real-time stress assessment using sliding window based convolutional neural network

Sensors (Switzerland), ISSN: 1424-8220, Vol: 20, Issue: 16, Page: 1-17
2020
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Article 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.

Bibliographic Details

Naqvi, Syed Faraz; Ali, Syed Saad Azhar; Yahya, Norashikin; Yasin, Mohd Azhar; Hafeez, Yasir; Subhani, Ahmad Rauf; Adil, Syed Hasan; Al Saggaf, Ubaid M; Moinuddin, Muhammad

MDPI AG

Chemistry; Computer Science; Biochemistry, Genetics and Molecular Biology; Physics and Astronomy; Engineering

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