Stress Monitoring Using HRV and Deep Recurrent Neural Networks for Safety in Workplace: A Comparative Analysis
Bibliographic record
Abstract
Workplace stress, a widespread issue in modern professional environments, significantly increases the potential for errors and accidents. Timely and precise stress identification is vital for fostering a secure and efficient work environment. This research introduces an innovative, comparative-analysis framework designed for real-time stress detection, utilizing Heart Rate Variability (HRV) as a reliable physiological indicator. Unlike standard heart rate measurements, HRV offers a granular view of the autonomic nervous system (ANS) function, enabling accurate stress evaluation. We implement a comprehensive methodology incorporating a refined preprocessing stage—including the removal of outliers, feature selection, and data normalization—along with a comparative assessment of eight deep recurrent neural network (RNN) architectures. These include vanilla RNN, bidirectional RNN (BiRNN), Gated Recurrent Unit (GRU), bidirectional GRU (BiGRU), standard Long Short-Term Memory network (LSTM), bidirectional LSTM (BiLSTM), Peephole LSTM, and Attention-based LSTM, applied to binary stress classification. Utilizing a dataset of 410,322 HRV records from the SWELL Knowledge Work (SWELL-KW) Dataset, our framework demonstrates exceptional performance, with the BiGRU architecture achieving a test accuracy of 99.51%. This study highlights the effectiveness of advanced temporal modeling and comparative analysis in creating robust stress detection systems for various occupational contexts, thereby enhancing workplace safety.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".