ForDigitStress: A multi-modal stress dataset employing a digital job interview scenario
This dataset addresses stress monitoring for researchers in affective computing, but it is incremental as it builds on existing stress datasets with a new scenario.
The authors tackled the problem of stress detection by creating a multi-modal dataset from digital job interviews, achieving a baseline classification accuracy of 88.3% and an F1-score of 87.5%.
We present a multi-modal stress dataset that uses digital job interviews to induce stress. The dataset provides multi-modal data of 40 participants including audio, video (motion capturing, facial recognition, eye tracking) as well as physiological information (photoplethysmography, electrodermal activity). In addition to that, the dataset contains time-continuous annotations for stress and occurred emotions (e.g. shame, anger, anxiety, surprise). In order to establish a baseline, five different machine learning classifiers (Support Vector Machine, K-Nearest Neighbors, Random Forest, Long-Short-Term Memory Network) have been trained and evaluated on the proposed dataset for a binary stress classification task. The best-performing classifier achieved an accuracy of 88.3% and an F1-score of 87.5%.