WESAD
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Affect recognition | WESAD (test) | Accuracy82.96 | 17 | |
| Activity Recognition | WESAD | Accuracy77 | 15 | |
| Emotion Distribution Learning | WESAD (subject-independent) | Chebyshev Distance0.0314 | 11 | |
| Emotion Distribution Learning | WESAD subject-independent (test) | Chebyshev Distance0.0314 | 11 | |
| Wearable stress and affect detection | WESAD | AUROC74.81 | 11 | |
| Binary Classification (Low/High Arousal) | WESAD (Leave-one-participant-out (LOPO)) | Balanced Acc66 | 7 | |
| Binary Classification (Low/High Valence) | WESAD Time-Aware (TA) | Balanced Accuracy64 | 7 | |
| Cross-modal knowledge transfer | WESAD ECG (Old) → PPG (New) (test) | BAcc49.57 | 6 | |
| Unsupervised cross-modal knowledge transfer | WESAD ECG -> PPG (test) | Balanced Accuracy49.57 | 6 | |
| Cross-modal knowledge transfer | WESAD PPG (Old) → ECG (New) (test) | BAcc62.96 | 5 | |
| Time-Series Segmentation | WESAD (test) | F-score64.1 | 4 | |
| Four-class classification | WESAD | F1 Score98.7 | 3 | |
| Valence classification | WESAD | F1 Score98 | 3 | |
| Unsupervised cross-modal knowledge transfer | WESAD PPG → ECG (test) | Balanced Accuracy62.96 | 2 | |
| Arousal classification | WESAD | F1 (EDA)83 | 1 |
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