A System To Detect Mental Stress Using Machine Learning And Mobile Development
Bibliographic record
Abstract
Hans Selye1coined, in 1936, the term “Stress” and definedit as “the non-specific response of the body to any demand for change.” Stress was generallyconsidered as being synonymous with distress. English language dictionaries (Oxford & Merriam-Webster) defined it as “physical, mental, or emotional strain or tension when a person perceives that demands exceed the personal and social resources the individual is able to mobilize.” Stress affects over 100 million Americans and is a driver of many chronic diseases. According to American Psychological Association (APA) 2012 study, “Stress is costing organizations a Fortune” and some cases as much as $300 billion a year. The challenges, importantly, for the individuals and the organizations are lack of proactive detection of the stress and inept preventive actions to manage mental health to circumvent adverse effects of the stress. This research paper addresses the challenge by developing and deploying machine learning enabled data driven & Electroencephalogram biosensor integrated mobile application that proactively gleans User’s stressful episodes, infuses collaborative intelligence derived from de-identified yet User relevant demographical, physiological, lifestyle and behavioral datasets and preventive healthcare insights to counter otherwise the long term negative effects of the stresson Users health. The paper presents prototyping solution as well as its application and certain experimental results.1Hans Selye was a pioneering Hungarian-Canadian endocrinologist. He conducted much important scientific work on the hypothetical nonspecific response of an organism to stressors - https://www.stress.org/what-is-stress/
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".