Bag-Level Multiple Instance Learning for Acute Stress Detection from Video Data

Nele Brügge, Alexandra Korda, Stefan Borgwardt, Christina Andreou, Giorgos Giannakakis, Giorgos Giannakakis, Giorgos Giannakakis, Heinz Handels, Heinz Handels

2025

Abstract

Stress detection is a complex challenge with implications for health and well-being. It often relies on sensors recording biomarkers and biosignals, which can be uncomfortable and alter behaviour. Video-based facial feature analysis offers a noninvasive alternative. This study explores video-level stress detection using top-k Multiple Instance Learning applied to medical videos. The approach is motivated by the assumption that subjects partly show normal behaviour while performing stressful experimental tasks. Our contributions include a tailored temporal feature network and optimised data utilisation by additionally incorporating bottom-k snippets. Leave-five-subjects-out stress detection results of 95.46 % accuracy and 95.49 % F1 score demonstrate the potential of our approach, outperforming the baseline methods. Additionally, through multiple instance learning, it is possible to show which temporal video segments the network pays particular attention to.

Paper Citation

in Harvard Style

Brügge N., Korda A., Borgwardt S., Andreou C., Giannakakis G. and Handels H. (2025). Bag-Level Multiple Instance Learning for Acute Stress Detection from Video Data. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 285-296. DOI: 10.5220/0013364900003911

in Bibtex Style

@conference{healthinf25,
author={Nele Brügge and Alexandra Korda and Stefan Borgwardt and Christina Andreou and Giorgos Giannakakis and Heinz Handels},
title={Bag-Level Multiple Instance Learning for Acute Stress Detection from Video Data},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={285-296},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013364900003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Bag-Level Multiple Instance Learning for Acute Stress Detection from Video Data
SN - 978-989-758-731-3
AU - Brügge N.
AU - Korda A.
AU - Borgwardt S.
AU - Andreou C.
AU - Giannakakis G.
AU - Handels H.
PY - 2025
SP - 285
EP - 296
DO - 10.5220/0013364900003911
PB - SciTePress