Transforming Healthcare with 5G-Driven Digital Twins: A Framework for Integrated Sensing and Communications in Fall Severity Detection
Bibliographic record
Abstract
The integration of Integrated Sensing and Communications (ISAC) systems with digital twin technology is revolutionizing human activity monitoring and fall severity classification. In this paper, we propose a cutting-edge framework that leverages 5 G -enabled devices, including smartphones, as dual-purpose platforms for sensing and communication. By utilizing real-time digital twin environments, a virtual 3D human model emulates various fall scenarios, generating a rich dataset for training machine learning models. A convolutional neural network (CNN) trained on this simulated dataset achieves an accuracy of 86.7% when validated with real-world measurements, showcasing the reliability and scalability of the approach. Smartphones equipped with 5G technology enable seamless deployment of this framework, providing ubiquitous access to advanced sensing capabilities while maintaining privacy and nonintrusiveness. This work underscores the transformative potential of combining digital twins with 5G ISAC systems to enhance healthcare applications and expand the role of consumer devices in advanced sensing
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".