SereniSens: a Multimodal AI Framework with LLMs for Stress Prediction through Sleep Biometrics
Bibliographic record
Abstract
Stress is a physiological and psychological response to threatening, challenging, or demanding situations. It can have profound effects on both mental and physical health such as anxiety and depression. Thus, managing stress levels in mental healthcare is crucial for maintaining overall well-being. In this work, we introduce SereniSens, a multimodal AI framework with conversational agent integration to predict stress levels during sleep using physiological data. We evaluated the performance of four machine learning models - Decision Tree, Support Vector Machines, Multilayer Perceptron, and XGBoost - on the SaYoPillow dataset containing physiological signals recorded during sleep. Our models achieve high accuracy in predicting fve stress levels, with SVM obtaining 99.37% accuracy, outperforming previous implementations. We also propose an architecture integrating these predictive models with a Large Language Model to create a context-aware chatbot for stress monitoring and management. This framework demonstrates the potential of AI in mental healthcare, particularly for personalized stress assessment and intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".