Introducing MDPSD, a Multimodal Dataset for Psychological Stress Detection

As we all know, long-term stress can have a serious impact on human health, which requires continuous and automatic stress monitoring systems. However, there is a lack of commonly used standard data sets for psychological stress detection in affective computing research. Therefore, we present a multimodal dataset for the detection of human stress (MDPSD). A setup was arranged for the synchronized recording of facial videos, photoplethysmography (PPG), and electrodermal activity (EDA) data. 120 participants of different genders and ages were recruited from universities to participate in the experiment. The data collection experiment was divided into eight sessions, including four different kinds of psychological stress stimuli: the classic Stroop Color-Word Test, the Rotation Letter Test, the Stroop Number-Size Test, and the Kraepelin Test. Participants completed the test of each session as required, and then fed back to us the self-assessment stress of each session as our data label. To demonstrate the dataset’s utility, we present an analysis of the correlations between participants’ self-assessments and their physiological responses. Stress is detected using well-known physiological signal features and standard machine learning methods to create a baseline on the dataset. In addition, the accuracy of binary stress recognition achieved 82.60%, and that of three-level stress recognition was 61.04%.