Enhancement of Stress Classification Using Web Camera-Based Imaging Photoplethysmography With a Frame Alignment Method

Stress is a mental health problem that is hazardous if not recognized early. A promising approach for noninvasive stress detection involves leveraging camera technology; however, there are notable challenges involved in this method, particularly regarding signal accuracy and quality, which are primarily caused by motion artifacts. This study aims to improve stress classification accuracy by using web camera-based imaging photoplethysmography signals. We introduce a frame alignment method that can significantly correct noise to mitigate motion artifacts. We use heart rate (HR) and HR variability metrics to monitor stress and classify the measurements into stress and no-stress conditions. The sample in this study comprises students who were stressed using the arithmetic task method. Our findings showed that the mean HR and low-frequency component increased under stressful conditions while the high-frequency component decreased. The primary contribution of this study involves refining the accuracy and reducing the time required for stress classification time. Notably, the proposed approach markedly improves the accuracy, with substantial noise correction in the resultant signal. We evaluate various classification methods, including logistic regression, support vector machine, na & iuml;ve Bayes, and random forest. Our results demonstrate that logistic regression achieved the highest accuracy of 98.5%, with a receiver operating curve value of 98%, an F1 score of 98.4%, and a computation time of 4.7 s. Overall, the proposed methodology holds considerable promise for camera-based noninvasive human stress assessment.