Identifying correlation between facial expression and heart rate and skin conductance with iMotions biometric platform
Bibliographic record
Abstract
Emotional reactions are stimulated when humans are presented with a stimulus, triggering a series of voluntary and involuntary responses. Human emotions can be measured from facial expressions and physiological processes. The iMotions biometric platform is able to detect and analyze the responses of different individuals, which are personalized. The iMotions software allows for the quantification of seven basic emotions: joy, sadness, anger, fear, contempt, surprise, and disgust. Along with iMotions, galvanic skin response (GSR) and heart rate sensors from the Shimmer Kit were used. GSR refers to the phenomenon wherein the skin temporarily becomes a better conductor of electricity due to elevated sweat gland activity. In this study, participants were shown videos associated with different emotions while their facial expressions were recorded and their heart rate/skin conductance data collected. Using iMotions and the Shimmer kit, this project aims to identify a possible correlation between the participants’ facial reactions and their physiological responses, namely, their heart rate and skin conductance, when exposed to different stimuli. The results indicated that there is a slightly higher correlation between emotion and GSR compared to emotion and heart rate. From the findings, it can be inferred that individuals react differently to the same stimulus. The iMotions software has great potential in forensic biometric analysis of human emotions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".