From Signals to Emotion: Affective State Classification through Valence and Arousal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, emotion recognition has become a promising area of research in the field of human-computer interaction. With the integration of Machine Learning (ML) techniques and Internet of Things (IoT) technologies, emotions can be identifed through facial expressions, voice, body language, and more. However, physiological signals are particularly valuable in this context due to their spontaneous and uncontrollable nature. In this paper, we describe the fundamental steps of an emotion recognition system based on physiological signals from preprocessing and feature extraction to feature selection through Principal Component Analysis (PCA) and classification using various ML models. We also propose a comparative analysis between different Artificial Intelligence (AI) models for classifying the four basic emotions: happiness, sadness, comfort, and anger. These emotions are represented by the commonly used dimensions of valence and arousal. Using the publicly available Database for Emotion Analysis using Physiological Signals (DEAP), our study yields interesting results. The Random Forest (RF) model achieved a top accuracy of 75.5% for arousal prediction using 25 PCA components, while K-Nearest Neighbors (KNN) reached 75% accuracy for valence prediction with 26 PCA components.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it