TITLE: ForDigitStress: A multi-modal stress dataset employing a digital job interview scenario 

ABSTRACT: 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%.
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%.
ForDigitStress: A multi-modal stress dataset employing a digital job interview scenario
http://arxiv.org/abs/2303.07742v1