Alcohol Impairment Detection Through Heart Rate Variability Analysis Using Gyrocarotidography
Bibliographic record
Abstract
Excessive alcohol consumption can impair decision-making and balance, putting workers and those around them at risk, especially in safety-sensitive occupations. Continuous monitoring of employees may help prevent workplace accidents. With advancements in wearable technologies such as smartwatches, headphones, and earbuds, there is a growing trend in real-time monitoring using wearable devices. To this end, this study introduces a carotid-mounted wearable system designed to assess heart rate variability (HRV) using an Inertial Measurement Unit (IMU) sensor for alcohol-induced impairment detection. The proposed system utilizes a novel signal modality called Gyrocarotidography (GCG)−a form of carotid ballistography (CBG) −to collect gyroscope-based signals and extract HRV features to analyze the effects of alcohol consumption while validating the results against a reference electrocardiogram (ECG) device. Statistically significant HRV features are then used to classify participants as impaired and non-impaired. The findings demonstrate a strong correlation between HRV features extracted from the IMU sensor and the ECG reference, confirming the feasibility of IMU-based HRV monitoring. Significant differences in HRV metrics before and after alcohol consumption further support the potential of this method, with the classification model achieving 83% accuracy. The proposed carotid-mounted IMU system offers a non-invasive, wearable solution for real-time HRV monitoring and alcohol impairment detection, and it provides clinicians and occupational health professionals with a tool for assessing alcohol-induced autonomic changes through HRV− offering a practical pathway for impairment detection and workplace safety enhancement.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".