Model adaptation and personalization for physiological stress detection

Aaqib Saeed, Tanir Ozcelebi, Johan Lukkien, Jan van Erp, Stojan Trajanovski

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

14 Citations (Scopus)


Stress and accompanying physiological responses can occur when everyday emotional, mental and physical challenges exceed one's ability to cope. A long-term exposure to stressful situations can have negative health consequences, such as increased risk of cardiovascular diseases and immune system disorder. It is also shown to adversely affect productivity, well-being, and self-confidence, which can lead to social and economic inequality. Hence, a timely stress recognition can contribute to better strategies for its management and prevention in the future. Stress can be detected from multimodal physiological signals (e.g. skin conductance and heart rate) using well-trained models. However, these models need to be adapted to a new target domain and personalized for each test subject. In this paper, we propose a deep reconstruction classification network and multi-task learning (MTL) for domain adaption and personalization of stress recognition models. The domain adaption is achieved via a hybrid model consisting of temporal convolutional and recurrent layers that perform shared feature extraction through supervised source label predictions and unsupervised target data reconstruction. Furthermore, MTL based neural network approach with hard parameter sharing of mutual representation and task-specific layers is utilized to acquire personalized models. The proposed methods are tested on multimodal physiological time-series data collected during driving tasks, in both real-world and driving simulator settings.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018
EditorsTina Eliassi-Rad, Wei Wang, Ciro Cattuto, Foster Provost, Rayid Ghani, Francesco Bonchi
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)978-1-5386-5090-5
Publication statusPublished - 31 Jan 2019
Event5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018 - Turin, Italy
Duration: 1 Oct 20184 Oct 2018


Conference5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018


  • Deep learning
  • Domain adaption
  • Multi-task learning
  • Personalization
  • Physiological stress
  • Temporal convolutional neural networks
  • personalization
  • multi-task learning
  • deep learning
  • physiological stress
  • domain adaption
  • temporal convolutional neural networks


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