A generic model of real-world non-ideal behaviour of FES-induced muscle contractions: simulation tool
Bibliographic record
Abstract
Functional electrical stimulation (FES) applications are frequently evaluated in simulation prior to testing in human subjects. Such simulations are usually based on the typical muscle responses to electrical stimulation, which may result in an overly optimistic assessment of likely real-world performance. We propose a novel method for simulating FES applications that includes non-ideal muscle behaviour during electrical stimulation resulting from muscle fatigue, spasms and tremors. A 'non-idealities' block that can be incorporated into existing FES simulations and provides a realistic estimate of real-world performance is described. An implementation example is included, showing how the non-idealities block can be incorporated into a simulation of electrically stimulated knee extension against gravity for both a proportional-integral-derivative controller and a sliding mode controller. The results presented in this paper illustrate that the real-world performance of a FES system may be vastly different from the performance obtained in simulation using nominal muscle models. We believe that our non-idealities block should be included in future simulations that involve muscle response to FES, as this tool will provide neural engineers with a realistic simulation of the real-world performance of FES systems. This simulation strategy will help engineers and organizations save time and money by preventing premature human testing. The non-idealities block will become available free of charge at www.toronto-fes.ca in late 2011.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".