Mental Stress Detection Using Physiological Signals Based on Soft Computing Techniques

This paper presents a novel approach for mental stress detection. In proposed system, three signals including Pupil Diameter (PD), Electrocardiogram (ECG) and Photoplethysmogram (PPG) are analyzed using the soft computing techniques, and most relevant features are extracted from each one. Then, the optimized features are selected by using the Genetic Algorithm (GA) and imported into the Fuzzy SVM (FSVM) to classify “stress” and “relaxation” states. In order to evaluate the performance of proposed system, a multimodal dataset consisting of pupil video, ECG and PPG signals are constructed; a Stroop color-word (SCW) test is designed to act as the stimulus to induce stress in healthy subjects. The experimental results demonstrate the physiological signals have great potential for stress detection, and the proposed system provides high classification performance.