Refinement of Bio-signal Data from Wearables for Stress Recognition Using Machine Learning and Transparent Artificial Intelligence
Bibliographic record
Abstract
This study explores the use of wearable devices for real-time detection of stress and evaluates the impact of meditation audio in alleviating stress following academic activities. It involves the collection of physiological signals specifically Heart Rate Variability (HRV) derived from Interbeat Intervals (IBI), Blood Volume Pulse (BVP), and Electrodermal Activity (EDA) during the Montreal Imaging Stress Task (MIST). To enhance the accuracy of stress classification, the study integrates a Genetic Algorithm with Mutual Information for efficient feature selection by minimizing redundancy. Additionally, Bayesian optimization is employed for fine-tuning the hyperparameters of machine learning models. Experimental results show that combining EDA, BVP, and HRV yields peak classification accuracies of 98.28% for two-level and 97.02% for three-level stress detection using the Gradient Boosting (GB) algorithm. When using only EDA and HRV, the system still performs well, achieving 97.07% and 95.23% accuracy for two- and three-level classifications, respectively. SHAP-based Explainable AI (XAI) analysis further confirms that HRV and EDA are the most influential features in determining stress levels. The study also demonstrates that meditation audio has a measurable calming effect, supporting its potential for stress management. These findings underscore the promise of integrating wearable technologies with machine learning for effective stress monitoring and intervention in academic settings. Keywords: Wearable devices, Real-time stress detection, Heart Rate Variability (HRV), Blood Volume Pulse (BVP), Electrodermal Activity (EDA), Montreal Imaging Stress Task (MIST), Genetic Algorithm, Mutual Information, Feature selection, Bayesian optimization, Machine learning, Gradient Boosting, Stress classification, SHAP, Explainable AI (XAI).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".