A Comparison of Closed-Loop Control Algorithms for Regulating Electrically Stimulated Knee Movements in Individuals With Spinal Cord Injury
Bibliographic record
Abstract
Functional electrical stimulation (FES) is the most commonly used technology for improving motor function in individuals who have spinal cord injury. Despite the wide range of FES applications reported in the literature, few electrical stimulation systems that can generate meaningful functional outcomes are currently available for use outside research laboratories. We tested proportional-integral-derivative, gain scheduling, and sliding mode control closed-loop control algorithms in a simulation of electrically induced knee extension against gravity to uncover some of the reasons why closed-loop control is not being more widely used in real-world FES systems. We also subjected the simulated FES system to muscle fatigue, muscle spasms, and the effects of muscle retraining. All of the controllers exhibited significantly degraded performance when these real-world nonlinear effects were included in the simulation. Moreover, all of the controllers were sensitive to variation in the parameters of the muscle recruitment function, which are subject to change during real-world FES use. We suggest several ways to improve the performance of closed-loop control algorithms for use in FES applications. We believe that closed-loop controllers have an important place in future FES applications, but the performance of these algorithms must be greatly improved before they can be implemented in real-world systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".