Human Mental State Monitoring in the Wild: Are We Better Off with DeeperNeural Networks or Improved Input Features?
Bibliographic record
Abstract
Advances in wearable devices have allowed for the collection of multimodal biomedical data from hundreds of subjects in everyday environments (i.e., in the wild). This has enabled the development of real-time monitoring of various human mental states, such as stress and anxiety, in highly ecological settings. Within a hospital setting, for example, this allows for prediction of burnout within medical staff, as well as anxiety within the patient population, thus improving their quality-of-life. Long-term monitoring via wearables has allowed for large amounts of data to be collected – so-called big data– and thus has opened doors for new applications relying on data-heavy deep learning algorithms. One question that remains unanswered, however, concerns the benefits of blindly applying deep learning algorithms with the collected data versus spend-ing some time and resources on feature engineering prior to machine learning. Feature engineering relies on domain knowledge to extract relevant parameters from the collected signals. In this paper, we aim to answer this question. In particular, we use a dataset collected from 200 hospital workers over a period of 10weeks during their work shifts. We compare the advantages of using data directly from the wearable devices and applying them to deep learning algorithms versus carefully-crafted features ap-plied to conventional machine learning algorithms. Experimental results are reported for stress and anxiety measurement from heart and breathing rate signals.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".