Exploring early stress detection from multimodal time series with deep reinforcement learning
2023 (English)In: Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 1917-1920Conference paper, Published paper (Refereed)
Abstract [en]
In our fast-paced world, timely access to information is essential. This urgency is highlighted in stress detection, where swift actions can mitigate harmful psycho-physiological effects. We introduce an early stress detection method using Deep Reinforcement Learning (DRL). This method utilizes DRL to efficiently analyze time series data segments, aiming for accurate and quick stress classification. We employ a dynamic observation window strategy, allowing the DRL agent to adjust based on data complexity. Our evaluations, performed on a public dataset using a Leave-One-Subject-Out (LOSO) method, emphasize DRL's potential in stress detection. The related code is available at https://github.com/cosbidev/DRL-4-Early-Stress-Detection.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 1917-1920
Keywords [en]
Deep Reinforcement Learning, Early Classification, Stress Detection, Time Series
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:umu:diva-221387DOI: 10.1109/BIBM58861.2023.10385549Scopus ID: 2-s2.0-85184866583ISBN: 9798350337488 (electronic)OAI: oai:DiVA.org:umu-221387DiVA, id: diva2:1840938
Conference
2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Istanbul, 5- 8 december, 2023.
2024-02-272024-02-272024-02-27Bibliographically approved