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WESAD — Wearable Stress and Affect Detection

Introduced by: Schmidt et al., 2018
DOI: 10.1145/3242969.3242985
Access: UCI Repository — free download, no auth required
Tier: 1

Overview

The most widely used public dataset for wearable stress detection. 15 subjects (2 excluded for device issues → 13 usable). Lab-based protocol with Trier Social Stress Test (TSST).

Signals

Modality Device Signals
Wrist Empatica E4 BVP (64 Hz), EDA (4 Hz), ACC (32 Hz), TEMP (4 Hz)
Chest RespiBAN ECG (700 Hz), EDA (700 Hz), EMG (700 Hz), RESP (700 Hz), TEMP (700 Hz), ACC (700 Hz)

Labels

  • Baseline (neutral)
  • Stress (TSST: public speaking + mental arithmetic)
  • Amusement (funny video)
  • Meditation (guided breathing)

Baselines

  • 3-class (baseline/stress/amusement): RF with wrist data ~75%
  • 2-class (stress/non-stress): RF with wrist data ~87%
  • Best chest results hit ~93%

Relevance

Closest public dataset to WHOOP signal types. Wrist EDA + BVP → HR, HRV, ACC, TEMP. 300+ citing papers with well-established benchmarks.

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