Analyzing the Relationship between the Autonomic Nervous System and Emotions Using High Temporal Resolution Capacitive Electrocardiography, Facial Expressions, and Respiration Data
Bibliographic record
Abstract
In the domain of emotional assessment, the predominant focus of numerous studies lies in elucidating the correlation between physiological indicators derived from electrocardiograms and facial expressions.However, there remains a scarcity of studies analyzing the correlation between autonomic nervous system indicators computed from electrocardiograms and emotions with high temporal resolution.In this study, we concurrently measured participants' facial images, capacitive electrocardiogram (cECG), and respiratory data.The cECG and respiratory data were sampled at 250 samples per second (sps), while facial images were captured at 5 frames per second (fps).By focus on respiration, our objective is to achieve a more nuanced understanding of the impact of emotions on the autonomic nervous system and the temporal sequence of responses.We devised a system to visually represent how elicited emotions are manifested in facial expressions, cECG, and respiratory data, with the aim of elucidating the intricate relationship among the autonomic nervous system, emotions, and breathing.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".