AI-enabled remote and objective quantification of stress at scale
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
Accurate measurement of human stress at scale is a major mHealth challenge. Here we explore the potential for deep neural networks (DNNs) to improve remote and objective quantification of stress from voluntary selfie videos captured through mobile device front-facing cameras. Two DNNs were trained with heart rate (HR) and heart rate variability (HRV) data obtained through photophlethysmographic imaging (PPGI) of 11,823 mobile device selfie videos captured in tandem with self-assessments of stress, and compared to contemporary algorithms used to estimate stress from HR and HRV data. A classification DNN and predictive DNN determined self-reported stress with 86 % accuracy and a mean absolute error of 0.001, respectively. Both DNNs performed far better than other recently described approaches when applied to the identical dataset. Well-trained DNNs can objectively and remotely quantify stress at scale. Future efforts may concentrate on the measurement of additional enigmatic cognitive states.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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