Affective State Recognition with Convolutional Autoencoders
Bibliographic record
Abstract
The aim of this study was to create a robust generalizable model to classify person's affective state based on physiological signals obtained using wearable sensor devices. Traditional machine learning methods require manual feature extraction from time sequences. Deep learning methods, such as Convolutional Neural Networks (CNN), can automatically extract features from time sequences. However, CNN models can be prone to overfitting, especially when the dataset is small. We apply a novel idea of using unsupervised convolutional autoencoders to automatically extract features from time-series data that are then fed to supervised classifier to classify people's affective state. We achieve almost 3% accuracy increase over traditional CNN model using all physio data from WESAD dataset, 2% increase using chest only physio data, and 8% increase using wrist only physio data while classifying neutral, stress, and amusement states. Code to reproduce the results can be found at https://github.com/srovins/wesad Clinical Relevance- A high-performing affective state recognition system can be utilized for various medical applications, ranging from patient monitoring to cognitive therapy.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".