Identification and Classification of Human Mental Stress using Physiological Data: A Low-Power Hybrid Approach

This paper proposes a framework using artificial intelligence for recognizing human mental stress based on physiological data. It comprises three stages; at first, emotion detection has been carried out from the facial image using a deep learning-based framework employing convolutional neural networks (CNN). Secondly, electrocardiogram (ECG) signals have been acquired with the help of the developed sensor-based system integrating Arduino UNO with AD8232 sensor. Finally, preprocessing has been performed and extracted features from the ECG signals are fed to support vector machines (SVM) for identification of arrhythmia and hence diagnosis of mental stress. All three aspects are connected together via the overall output calculation. A score is added in accordance to the output for each of these three stages. The range of the final score (i.e., the overall output) decides the level of mental stress suffered by the person. The proposed method has been trained on FER-2013 for emotion detection and on MIT-BIH for arrhythmia detection. It is found that the performance outperforms other machine learning and deep learning models without much compromise in accuracy. The proposed method can perform classification based on the patient's current condition and gives an insight of the degree and time duration of stress of the patient.