Cyber-Physical System Framework for Measurement and Analysis of Physical Activities
Bibliographic record
Abstract
Several recent studies in Cyber-Physical Systems (CPS) focus on monitoring human movement and capturing data for further processing and analysis. However, there is a lack of studies that address the configurability and modularity of these systems, which is important for designing customized systems with customized devices. We propose a solution to solve this through a modular framework that automatically recognizes and configures new devices and provides real-time data wirelessly. The proposed framework creates a Digital Twin of the physical device and mirrors its attributes and sensory information into the cyber world so they can be used in real-time and post-routine analysis. As a proof of concept, a configurable CPS model for physical activities monitoring is designed and implemented. The designed gait monitoring and analysis system delivers spatiotemporal data from multiple multi-sensory devices to a central data handling and backup cloud server over conventional IEEE802.11 Wi-Fi. An experiment involving a young athlete examined whether or not the CPS components would recognize each other over foreign networks and communicate accurate information.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".