WESAD Baseline Implementations
Dataset: wesad
Paper: schmidt-2018-wesad
Available Baselines
The WESAD paper provides reference implementations for: - K-Nearest Neighbors (KNN) - Linear Discriminant Analysis (LDA) - Random Forest (RF) - Decision Tree (DT) - AdaBoost (AB)
Where to Find Code
- UCI repository includes sample code
- Numerous open-source implementations on GitHub (search "WESAD")
- Many papers publish their WESAD-specific code
Known Results (Wrist-only)
| Model | 3-Class | 2-Class |
|---|---|---|
| RF | ~75% | ~87% |
| AB | ~75% | ~85% |
| LDA | ~72% | ~84% |
Linked To
- Dataset: wesad
- Dataset: feel-benchmark (WESAD included)
- Blog: empatica-e4