Affective Computing Out-of-The-Lab: The Cost of Low Cost
Bibliographic record
Abstract
Affective computing, with its potential to enhance human-computer interaction, is experiencing an expansion of its use in many areas such as health care and the gaming industry. One obstacle to its widespread adoption can be the high cost requirement for biofeedback. Indeed, typical laboratory setups are often expensive which makes them out of reach for many. This paper explores lower-cost alternatives to expensive laboratory solutions. Data from several recent studies totaling over 200 hours of physiological recordings are leveraged to compare high-end solutions to a lower cost one. Heart rate, electrodermal activity, facial action units, head movement, and eye movement - five of the most used bio-behavioural signals - have their respective higher and lower cost sensors compared. The resulting comparison illustrates that lower-cost solutions are not drop-in replacements. While a correlation of 0.62 between electrodermal activity readings was found, notable differences between reported heart rate readings over small timescales were also observed. Head tracking recordings shared similarity (0.51), but eye tracking did not (0.18). As for facial action units recognition, only those linked to smiling had significant correlation (around 0.48). These results should broaden the range of contexts in which biofeedback could be exploited. This aim may be fulfilled by informing the reader of the extent of lower cost solution applications.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.017 |
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".