Detection and Classification of Mental Stress for Prognosis of Anxiety Levels
Bibliographic record
Abstract
Stress is characterized by mental or emotional unease, and in today's demanding environments, persistent workloads and strict deadlines often lead to overlooked stress management. This neglect may increase the risk of cardiovascular problems and subsequently diminish overall health. To address this, we develop a machine learning approach capable of detecting and categorizing stress into three classes: no stress, time pressure, and interruption. We compared the performance of several algorithms, including Logistic Regression, Gaussian Naïve Bayes (GNB), and K-Nearest Neighbors (KNN). Our experiments showed that KNN provided superior accuracy for this classification task. The SWELL dataset contains recordings of heart rate variability (HRV), as well as accompanying measures such as facial movements, posture, ECG signals, and skin conductance. Our results indicate potential applications for individualized intervention strategies, real-time monitoring platforms, and preventive mental health care by enabling earlier recognition and prompt assistance for those under stress. Using the SWELL dataset, our system attained an accuracy of 99.48 %, demonstrating robust performance in differentiating stress categories.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".