A Wearable Wisdom: ABI-Modal Behavioral Biometric Scheme for Smartwatch User Authentication
Bibliographic record
Abstract
This work utilizes wearable devices for real-time stress detection and investigates the effectiveness of meditation audio in reducing stress levels after academic exposure. Physiological data, including Interbeat Interval (IBI)-derived Heart Rate Variability (HRV), Blood Volume Pulse(BVP),andelectrodermalactivity(EDA),arecollectedduringthe Montreal Imaging Stress Task (MIST). The stress classification methodology employs an integrated approach using Genetic Algorithm andMutualInformationtoreducefeaturesetredundancy.Itfurtheruses Bayesian optimization to fine-tune machine learning hyperparameters. The results indicate that the combination of EDA, BVP, and HRV achievesthehighestclassificationaccuracyof98.28%and97.02%using the Gradient Boosting (GB) algorithm for 2-level and 3-level stress classification. In contrast, EDA and HRV alone achieve a comparable accuracy of 97.07% and 95.23% for 2-level and 3-level stress classification, respectively. Furthermore, the SHAP Explainable AI (XAI) analysis confirms that HRV and EDA are the most significant features for stress classification. The study also finds evidence that listening to meditation audio reduces stress levels. These findings highlight the potential of wearable technology combined with machine learning for real-time stress monitoring and management in academic environments.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".