Publication
Bag-Level Multiple Instance Learning for Acute Stress Detection from Video Data
Nele Brügge; Alexandra Korda; Stefan Borgwardt; Christina Andreou; Giorgos Giannakakis; Heinz Handels
In: Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF. International Conference on Health Informatics (HEALTHINF-2025), February 19-22, Porto, Portugal, ISBN 978-989-758-731-3, SciTePress, 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.