Deep Learning Framework for Classification of Mental Stress from Multimodal Datasets
Bibliographic record
Abstract
A healthy society must take proper measures to handle human stress, a severe health risk.To classify felt mental stress, this work offers an experimental inquiry to determine the proper phase when electroencephalography (EEG) based input from the DEAP dataset is combined with accelerometer sensor data from the WESAD dataset.A multisensory data fusion approach has been proposed to gather complete data for prognostic modeling and analysis.These techniques attempt to create a composite health index (HI) by fusing numerous sensor inputs.To get an aggregated version of the EEG-based data from the DEAP dataset and Accelerometer (ACC) sensor data from the WESAD dataset, we used the k-medoid data aggregation method with time-frame constrained intra-cluster similarity computations.The mental state is then classified into low-stress, medium-stress, and highstress categories using a CNN trained on this aggregated dataset.Three types of data low stress, medium stress, and high stress, are created.To categorize stress levels, we used three classifiers Support Vector Machine (SVM), Logical Regression (LR), and Naï ve Bayes (NB) are used.Three-class stress classification is accurate to 82.85% of the actual value.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".