A novel system using Fuzzy SVM to analyze pupil diameter, ECG, and PPG signals provided high classification performance for detecting mental stress induced by a Stroop color-word test.
Does a system using PD, ECG, and PPG signals with soft computing techniques accurately detect mental stress in healthy subjects?
A novel soft computing approach using PD, ECG, and PPG signals provides high classification performance for detecting mental stress.
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.
Mokhayeri et al. (Thu,) conducted a other in Mental stress. Fuzzy SVM classification using PD, ECG, and PPG signals was evaluated on Classification of stress and relaxation states. A novel system using Fuzzy SVM to analyze pupil diameter, ECG, and PPG signals provided high classification performance for detecting mental stress induced by a Stroop color-word test.