Efficient Prediction of Blood Alcohol Level Using ML and Accelerometer Data
Bibliographic record
Abstract
IoT sensors are extensively used in a variety of medical applications such as patient monitoring. In recent years, multiple papers have been published on the measurement of blood alcohol level (BAL) using more specialized biosensors. Transdermal alcohol content (TAC), a practical and non-invasive tool for measuring BAL, was employed in several recent research works. As BAL affects the person's way of walking, accelerometers (ACC) combined with TAC data fed to machine learning algorithms (MLA) were applied to predict the state, drunk or sober, of a person. However, the accuracy of prediction was not high enough to be considered reliable. In this paper, using an archived “BAR CRAWL” dataset, we implemented five MLA: Linear Discriminant Analysis (LDA), Decision Tree (DT), Random Forest (RF), Extra Trees (ET), and Ada Boost (AB), with a variety of features to accurately predict the state of a person based on his alcohol levels. Furthermore, we defined various alcohol thresholds to predict the level of intoxication. Also, we were able to identify the intoxicated person. Our experimental results showed that we can achieve up to 28.9 % higher prediction accuracy with less processing time (PT) when compared to previous published works using the same dataset.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.007 |
| 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".