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Schmidt 2018 — WESAD: Wearable Stress and Affect Detection

Citation: Schmidt, P., Reiss, A., Duerichen, R., et al. (2018). Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2018).
DOI: 10.1145/3242969.3242985
Type: Dataset paper

Key Contribution

Introduced the most widely used public dataset for wearable stress detection. Dual-device (wrist + chest) protocol with validated stress induction.

Design

  • 15 subjects, lab-based
  • Trier Social Stress Test (public speaking + mental arithmetic)
  • Amusement condition (funny video)
  • Meditation (guided breathing)
  • Two wearable devices simultaneously

Key Results

  • Chest-worn: 3-class ~93%, 2-class ~93%
  • Wrist-worn (E4): 3-class ~75%, 2-class ~87%
  • Random Forest best overall

Impact

300+ citations. Standard benchmark for wearable affect recognition. Enabled the entire field of wrist-based stress detection research.

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