AI-Driven Multimodal Mental Health Monitoring System Using Emotional, Physiological and Self-Reported Data
Bibliographic record
Abstract
Mental health challenges are on the rise, driven by modern lifestyles, workplace pressures, and social stressors.Conventional assessments often rely on self-reported questionnaires and occasional clinical visits, which can miss real-time changes and sometimes lack objectivity.To address this gap, we propose an AI-driven mental health monitoring system that uniquely integrates multiple non-invasive modalities: facial expression recognition, respiration analysis, infrared body temperature sensing, and the Questionnaire-based Model.This multimodal approach allows for continuous and holistic assessment of both emotional and physiological states.Facial expression analysis helps identify emotions such as happiness, sadness, anger, and stress; respiration monitoring captures irregular breathing patterns linked to anxiety; and temperature sensing highlights stress-induced variations.These signals are further complemented by a psychological questionnaire, which achieved the highest predictive accuracy of 93%, underscoring its effectiveness when combined with physiological cues.By uniting these different perspectives, our system not only improves detection accuracy but also reduces bias, offering a more reliable tool for early mental health intervention compared to traditional single-method approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".