A Review on Sensor-based HAR Models Using GNN: AI in Healthcare
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
Graph Neural Networks (GNNs) have emerged as a transformative force in sensor-based Human Activity Recognition (HAR) by capturing complex spatial and temporal dependencies. In the era of Healthcare 5.0, where AI-driven workspaces and sustainable healthcare converge, the application of GNNs for HAR holds promise for enhancing patient monitoring, fall detection and personalized care. This paper systematically reviews recent advancements in GNN models specifically tailored for sensor-based HAR, examining methodologies from data acquisition to activity classification. By aligning these technological advances with AI-driven healthcare objectives, the review aims to outline the benefits, challenges and future directions of integrating GNNs into sustainable healthcare ecosystems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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