A predictor-based compensation for electromechanical delay during neuromuscular electrical stimulation-II
Bibliographic record
Abstract
Neuromuscular electrical stimulation (NMES) is a promising technique to restore functional mobility in persons with paraplegia. Closed-loop control of NMES allows precise and accurate limb control in critical tasks such as gait restoration or gait retraining. However, a major cause of degraded performance and instability during NMES control is electromechanical delay (EMD). Uncertainty, exogenous disturbances (e.g., spasticity), and particularly, unknown nonlinear muscle force-length and muscle force-velocity relationships in the musculoskeletal system complicate control design to compensate for EMD. Despite these difficulties, a predictor-based control method that compensates for EMD in the uncertain musculoskeletal system is developed in this paper. The developed control method is an improvement over our previous work in which only proportional-derivative (PD)-type delay compensating controller was developed for a completely unknown nonlinear musculoskeletal system. The result incorporates integral control in the previous PD-based control design (i.e., a PID-type delay compensating controller is developed) and is shown to achieve uniformly ultimately bounded tracking.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".