A Fusion of Wavelet-based and Unsupervised Machine Learning Method for Artifacts Removal in Electrodermal Activity Signal
Bibliographic record
Abstract
The fast tempo of modern society has brought people a series of emotional changes and mental pressures. Therefore, research have emerged to help people pay attention to and regulate their mental health. Physiological signals are used in studies from different fields to monitor and detect emotional change and stress. Electrodermal activity (EDA) is such a physiological signal that can reflect changes in skin conductivity when people's emotions change. The nature of neuromodulation makes such changes not easily controlled by people subjectively so EDA is an ideal emotion and stress monitoring indicator. Especially with the popularity of wearable devices in the market, wearable devices with built-in EDA sensors will be more competitive for the functions that help people regulate their mental health. However, since the EDA sensor usually acquires signals through fingers, palms, or wrists, artifacts will inevitably be generated when users move their hands or arms, and the artifacts will affect the accuracy of emotional change detection. As a result, removing artifacts in EDA signals is a challenging and important topic. In this work, multiple signal processing methods are applied to realize the objective of removing artifacts in the EDA signals from the AMIGOS dataset. The results show that the proposed method is promising and has the potential to be utilized in real-time EDA signal processing and emotional change detection applications.
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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 | 0.001 | 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 | 0.000 | 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) | 0.003 | 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 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".