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

arXiv: https://arxiv.org/abs/2303.07742 · HTML: https://mlsanity.com/paper/2303.07742/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 63 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](https://mlsanity.com/paper/1911.00133/) (2019)

## Built on by (most cited first, 3 of 3)

- [Stressor Type Matters! -- Exploring Factors Influencing Cross-Dataset Generalizability of Physiological Stress Detection](https://mlsanity.com/paper/2405.09563/) (2024)
- [Socially Interactive Agents for Robotic Neurorehabilitation Training: Conceptualization and Proof-of-concept Study](https://mlsanity.com/paper/2406.12035/) (2024)
- [AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI](https://mlsanity.com/paper/2508.02680/) (2025)

## Abstract (arXiv)

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%.