Multi-Class Stress Detection Using Electrodermal Activity: Evaluation of A Hybrid Model Using UBFC-Phys Dataset

Stress is a psychological and physiological response to internal or external pressures or challenges that exceed an individual's ability to cope. In response to these conditions, the human body produces physiological signals that reflect its internal states, often without conscious awareness. Among these signals, electrodermal activity (EDA) is known for its sensitivity to changes in stress levels. Traditional methods for assessing stress, such as questionnaires and self-reports, remain widely used, but they are often influenced by subjectivity and recall bias. This has led to increased interest in objective, data-driven approaches. This research proposes a hybrid stress detection model. The EDA signals were used from the UBFC-Phys dataset, which comprises data from 56 participants under three conditions: rest, moderate stress, and high stress. The signals were preprocessed using filtering, smoothing, and subject-level normalization to reduce inter-individual variability. A set of features was extracted, such as tonic component features. Multiple experiments were conducted on baseline, deep learning, and hybrid models not only for stress detection classification purposes but also for performance evaluation purposes. The proposed model achieved an accuracy of 92.16% in the multi-class classification task. The findings of this research contribute to mental health and well-being by providing an accurate model for stress detection that can be adapted to healthcare, education, and workplace settings, ensuring a healthier and more sustainable future.

@article{Alsayed2026,
  title     = {Multi-Class Stress Detection Using Electrodermal Activity: Evaluation of A Hybrid Model Using UBFC-Phys Dataset},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Kawther Alsayed and Hamza Ghandorh and Wael M.S. Yafooz},
  doi       = {10.14569/IJACSA.2026.0170628},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170628}
}