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

Sensors

Yasir Hafeez¹  SyedSaad Azhar Ali¹  AhmadRauf Subhani¹  Norashikin Yahya¹  SyedFaraz Naqvi¹  Muhammad Moinuddin²  UbaidM Al Saggaf²  SyedHasan Adil³  MohdAzhar Yasin⁴ 

[1] Center for Intelligent Signal and Imaging Research (CISIR), Electrical and Electronics Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia;Center of Excellence in Intelligent Engineering Systems, Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudia Arabia;Department of Computer Science, Iqra University, Karachi 75500, Pakistan;Department of Psychiatry, Universiti Sains Malaysia Health Campus, Kota Bharu 16150, Malaysia;

关键词: stress-assessment;    CAD (computer-aided diagnosis);    machine learning;    convolutional neural network;    feature extraction;    real time;   

DOI  :  10.3390/s20164400

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown