Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wearable technology is growing in popularity, and wearable devices, such as smartwatches, are used in many applications, from fitness tracking and activity recognition to health monitoring. As the affordability and popularity of such devices increase, so does the amount of personal and unique data that they provide. At the same time, advantages in microprocessor and memory technology enable multiple physiological signal sensors integrated into wearable devices to collect personal and unique data. After the data is extracted, machine learning classification algorithms can help investigate the insights of the data. In this work, we examine the performance of a real-time stress detection system based on physiological signals collected from wearable devices. Specifically, three physiological signals, electrodermal activity (EDA), electrocardiogram (ECG), and photoplethysmo-graph (PPG) that can be collected through smartwatches, are examined for stress classification. Six machine learning methods are used for the classification in a post-acquisition phase, at a computer, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Naive Bayes, Logistic Regression, and Stacking Ensemble Learning (SEL). Data from two publicly available datasets are used for training and testing. We examine the accuracy of each modality and the combination of all modalities. According to evaluation results, EDA has the best accuracy when SEL is used for classification. Also, the accuracy of EDA outperforms the other signals and combinations, in comparison with any of the other machine learning approaches, for both datasets. EDA collected from the wearable device has a great potential to be used for a real-time stress detection system.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it