Identifying correlation between facial expression and heart rate and skin conductance with iMotions biometric platform
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
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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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 | 0.001 | 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 it