Video-based contactless detection of task-related concentration using advanced machine-learning techniques
Bibliographic record
Abstract
The present study aimed to test the accuracy of applying machine learning to a novel contactless video-based approach in detecting task-related concentration. Evaluations of concentration on-task have relied on laboratory methodologies, which encounter difficulties when applied to real work scenarios. Video photoplethysmography (VPPG) can present a solution to these difficulties by extracting physiological changes from videos captured by any conventional camera. Applying machine learning to physiological signals from VPPG can enable contactless detection of task-related concentration. Thirty adults completed a simulated task. Physiological changes were recorded via electrocardiogram (ECG) and VPPG. Pre-trained VGG, support vector machine, and XGBoost were performed on ECG and VPPG signals to detect when participants were on- or off-task. The ensemble method, which combined three machine-learning methods, applied to VPPG signals proved to be highly accurate (∼97%). Among individual machine-learning methods, pre-trained VGG applied to VPPG signals performed the best, comparable to the ensembled method. All analyses showed detection based on VPPG signals to significantly outperform ECG signals. Results establish a proof-of-concept that VPPG and machine learning can be used to detect task-related concentration in a contactless, convenient, and inexpensive fashion. VPPG can enable the detection of task-related concentration in natural work settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".