Comprehensive Literature Review on Large Language Models and Smart Monitoring Devices for Stress Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This review evaluates the complementary roles of Large Language Models (LLMs) and smart monitoring devices in stress management, aiming to identify synergies for improved mental health solutions. It explores the evolution of LLM architectures (Encoder-Decoder, Encoder-Only, Decoder-Only, and Mixture of Experts), emphasizing their strengths in personalized interventions. Simultaneously, it examines physiological monitoring technologies, ranging from traditional biometric sensors to AI-enhanced platforms, focusing on bioelectrical signals (ECG, EEG, EMG, GSR) and optical measurements (PPG, BVP, SpO2). By detailing the principles, devices, and stress monitoring applications of these modalities, the review uncovers opportunities for integration. The findings provide a foundation for future research into AI-driven, multimodal stress management systems, promoting more effective and accessible mental health support.
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.
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 it