A Product Fuzzy Convolutional Network for Detecting Driving Fatigue
Bibliographic record
Abstract
Existing driving fatigue detection methods rarely consider how to effectively fuse the advantages of the electroencephalogram (EEG) and electrocardiogram (ECG) signals to enhance detection performance under noise conditions. To address the issues, this article proposes a new type of the deep learning (DL) framework based on EEG and ECG called the product fuzzy convolutional network (PFCN). It should be noted that this article first investigates how to fuse EEG and ECG signals to deal with driving fatigue detection under noise conditions in both simulated and real-field driving environments. Specifically, the PFCN includes three subnetworks. The first uses a fuzzy neural network (FNN) with feedback and a product layer, effectively capturing the particularity and temporal variation of high-dimensional EEG signals and reducing the time-space complexity. The second subnetwork uses a 1-D convolution to convert the ECG data into feature sequences, providing high accuracy and low computational complexity in ECG data classification. The third subnetwork proposes a fusion-separation mechanism to effectively fuse the extracted ECG and EEG features, suppressing the noise interference and ensuring higher detection accuracy. To evaluate the performance of PFCN, a series of experiments has been set up in both simulated and real-field driving environments. The results indicate that the proposed PFCN model has better robustness and detection accuracy compared with several mainstream fatigue detection models.
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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 | 0.001 |
| 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) | 0.001 | 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".