Non-Interference Driving Fatigue Detection System Based on Intelligent Steering Wheel
Bibliographic record
Abstract
Driving fatigue is an important factor leading to traffic accidents. For this reason, we propose a non-interference fatigue detection system, which consists of a steering wheel embedded with an electrocardiogram (ECG) acquisition device and an ECG fatigue detection model. By holding the steering wheel with the driver’s palm, the system can collect their ECG signals and transmit them to the fatigue detection model for tiredness analysis after preprocessing. In particular, the proposed ECG fatigue detection model is composed of a simulation generation module based on a cycle-generative adversarial network (CycleGAN) and a fatigue detection module based on a fuzzy convolution neural network (FCNN). The acquired palm signal is fed into the simulation generation module to generate a clearer chest-like signal, thereby improving the final task performance. In addition, a new FCNN is posed to analyze the simulated chest signal to focus on the time variation and ignore the specificity of the ECG signal, therefore increasing the robustness of the system. The experimental results show that the proposed fatigue detection model has good stability and accuracy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".