FES standing up in paraplegia: a comparative study of fixed parameter controllers
Bibliographic record
Abstract
A computer model was developed as a test bed to aid the development and testing of FES controllers for assisting the sit to stand maneuver. Here, the authors illustrate its use by comparing the performance of two basic open-loop techniques used clinically and three experimental closed-loop control strategies. The simpler open loop control described by Kralj and Bajd (1989), was robust with a rapid response which can produce undesirable large terminal velocities in the knee joints. A variant of the scheme, often used in clinical practice was to linearly increase the stimulus intensity. This could be more convenient for the subject but unfortunately also deteriorated the control performance. The closed-loop controllers can reduce the terminal angular velocities of the knee joints. The phase-plane on-off controller, although easy to design, elongated the standing up time and resulted in increased arm forces. The PID controller was robust, but in order to optimize its performance, gain scheduling was required for the different phases of the motion. The fuzzy logic controller was able to accommodate considerable changes in the system dynamics. It produced a smooth motion with a superior trajectory following capability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".