Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network
Syed Faraz Naqvi, Syed Saad Azhar Ali, Norashikin Yahya, Mohd Azhar Mohd Yasin, Yasir Hafeez, Ahmad Rauf Subhani, Syed Hasan Adil, Ubaid M. Al‐Saggaf, Muhammad Moinuddin
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.