Modeling and analysis of real-time issues in rehabilitation robotic systems using coloured Petri nets
Bibliographic record
Abstract
In this paper, we discuss some of the real-time issues that must be addressed in the design of rehabilitation robotic systems. A Coloured Petri Net (CPN) model is developed to analyze critical timing and resource requirements for achieving reliable and safe performance. A CPU scheduling problem is introduced and analyzed. An example of a robotic system treating injured or impaired limbs is used as a study case. This work shows the advantages of using CPN to model and simulate the robot-control system in real-time. This work is part of a research and development project to design and build an intelligent robot for physical and occupational therapy to help limb recovery. It is assumed that both the position and velocity of the patient's limb are measurable and controllable in real-time. The therapy process duration and frequency of limb movement are also controlled by the robot-control system. A time concept is introduced to the design to investigate the performance of the control system. Compact and extendable system design is achieved by utilizing the hierarchical aspect of CPN. This paper presents simulation results that demonstrate satisfactory mission critical performance of the system.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".