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Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network.

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.

SUBMITTER: Naqvi SF 

PROVIDER: S-EPMC7472011 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network.

Naqvi Syed Faraz SF   Ali Syed Saad Azhar SSA   Yahya Norashikin N   Yasin Mohd Azhar MA   Hafeez Yasir Y   Subhani Ahmad Rauf AR   Adil Syed Hasan SH   Al Saggaf Ubaid M UM   Moinuddin Muhammad M  

Sensors (Basel, Switzerland) 20200807 16

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.  ...[more]

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