Enhanced ALIVE Mind Controller and Machine Learning to Detect Drowsiness While Driving
Bibliographic record
Abstract
Thousands of road accidents occur each year, one of the main causes being drowsiness at the wheel. Therefore, this paper proposes an enhanced scheme called ALIVE Mind to detect drowsy drivers and take action on their car using Machine Learning and EEG. For that matter, we built and designed a circuit board called AMC 2.0 that allows a simple EEG headset to read brain waves from a driver and send this data to a computer via Bluetooth. Those signals are then saved and analyzed by a deep neural network to find if the driver is drowsy or is about to sleep. To evaluate our solution, we conducted simulations and collected brain signals of a subject driver while on a driving simulator. With those values, we were able to train a model to detect whether the subject was drowsy or awake. Finally, when the system detects that the driver is in a fatigued state, it can take control of the car to park it in a safe place. Preliminary results show the effectiveness of the ALIVE Mind project and how it outperforms previous works in terms of minimizing computational needs and improving the prototype's convenience and suitability for car constructors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".