iAware: A Real-Time Emotional Biofeedback System Based on Physiological Signals
Why this work is in the frame
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Bibliographic record
Abstract
Self-awareness is the foundation of emotional intelligence. Most people can recognize their own and others' emotions. However, many people suffer from a common infirmity that prevents them from recognizing emotion within themselves and are therefore unable to experience a life that fulfills them emotionally. We propose a real-time mobile biofeedback system that uses wearable sensors to depict five basic emotions and provides the user with emotional feedback. We also present empirical results for the configuration of a physiological signal-based emotion recognition system in two experimental scenarios involving controlled and noncontrolled environmental settings. In our evaluation, we show that iAware helps increase emotional self-awareness by reducing the predictive error by 3.333% for women and by 16.673% for men. The primary results suggest the usefulness and necessity of the iAware system to provide users with real-time biofeedback based on physiological signals.
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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 | 0.000 | 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) | 0.016 | 0.016 |
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