Prediction of Emotional Measures via Electrodermal Activity (EDA) and Electrocardiogram (ECG)
Bibliographic record
Abstract
Affect recognition is a signal and pattern recognition problem that plays a major role in affective computing. The affective state of a person reflects their emotional state, which could be measured through the arousal and valence dimensions, as per the circumplex model. We attempt to predict the arousal and valence values by exploiting the Remote Collaborative and Affective Interactions (RECOLA) data set RECOLA is a publicly available data set of spontaneous and natural interactions that represent various human emotional and social behaviours, recorded as audio, video, electrodermal activity (EDA) and electrocardiogram (ECG) biomedical signals. In this work, we focus on the biomedical signal recordings contained in RECOLA. The signals are processed, accompanied with pre-extracted features, and accordingly labelled with their corresponding arousal or valence annotations. EDA and ECG features are fused at feature-level. Ensemble regressors are then trained and tested to predict arousal and valence values. The best performance is achieved by optimizable ensemble regression, with a testing root mean squared error (RMSE) of 0.0154 for arousal and 0.0139 for valence predictions. Our solution has achieved good prediction performance for the arousal and valence measures, using EDA and ECG features. Future work will integrate visual data into the solution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".