Guest Editorial AI-Empowered Internet of Things for Data-Driven Psychophysiological Computing and Patient Monitoring
Bibliographic record
Abstract
As The cornerstone of human health, physical and mental well-being are intricately linked, influencing both an individual's physical condition and their emotional state [1]. Chronic diseases such as hypertension and diabetes can have a significant impact on mental health, leading to anxiety and depression [2]. Similarly, psychological problems such as stress, anxiety, and depression can weaken the immune system, making individuals more susceptible to physical illnesses. In recent years, the rapid development of technology has brought exciting new possibilities to the field of physical and psychological health. The Internet of Things (IoT) and artificial intelligence (AI) have shown great potential in building a comprehensive health management system that empowers individuals to take a more proactive role in their well-being.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.024 | 0.020 |
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".