@article{scholars12828,
          number = {16},
          volume = {20},
            note = {cited By 9},
             doi = {10.3390/s20164400},
           title = {Real-time stress assessment using sliding window based convolutional neural network},
            year = {2020},
         journal = {Sensors (Switzerland)},
       publisher = {MDPI AG},
           pages = {1--17},
            issn = {14248220},
          author = {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.},
        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},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089240804&doi=10.3390\%2fs20164400&partnerID=40&md5=5d5ff503da4a2e79886a5ffb96d6d2d7},
        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. {\^A}{\copyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.}
}