A System To Detect Mental Stress Using Machine Learning And Mobile Development
Bibliographic record
Abstract
Hans Selye <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> coined, 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. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Hans 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/
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".