Classification of Human Mental Stress Levels Using a Deep Learning Approach on the K-EmoCon Multimodal Dataset
Bibliographic record
Abstract
The idea of "stress stacking" is that the psychological stress many people face in modern society can lead to depression, heart disease, and cancer, among other long-term illnesses.Thus, managing and tracking a person's stress is crucial.This paper proposes that a modified Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model can extract features from Electroencephalogram (EEG), Electrocardiogram (ECG), and Accelerometer (ACC) data.A lengthy feature vector combines relevant features from the various modalities.Combining all of the features could improve classification performance, but it could also increase the number of dimensions and lead to bad performance because of redundant information and inefficiency.This paper uses Kruskal-Wallis analysis to suggest a new way to deal with the high-dimensionality problem by automatically finding the best subset of features.To categorize the stress based on the feature vector, we utilized a K-Nearest Neighborhood (KNN), a Random Forest (RF), a Support Vector Machine (SVM), and a Decision Tree Classifier (DT).SVM outperformed the other three classifiers with a performance accuracy of 94.58%, which is 3.72% Superior to cutting-edge techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".