Comparison of strategies and performance of functional electrical stimulation cycling in spinal cord injury pilots for competition in the first ever CYBATHLON
Bibliographic record
Abstract
Functional Electrical Stimulation (FES) can elicit muscular contraction and restore motor function in paralyzed limbs. FES is a rehabilitation technique applied to various sensorimotor deficiencies and in different functional situations, e.g. grasping, walking, standing, transfer, cycling and rowing. FES can be combined with mechanical devices. FES-assisted cycling is mainly used in clinical environments for training sessions on cycle ergometers, but it has also been adapted for mobile devices, usually tricycles. In October 2016, twelve teams participated in the CYBATHLON competition in the FES-cycling discipline for persons with motor-complete spinal cord injury. It was the first event of this kind and a wide variety of strategies, techniques and designs were employed by the different teams in the competition. The approaches of the teams are detailed in this special issue. We hope that the knowledge contained herein, together with recent positive results of FES for denervated degenerating muscles, will provide a solid basis to encourage improvements in FES equipment and open new opportunities for many patients in need of safe and effective FES management. We hope to see further developments and/or the benefit of new training strategies at future FES competitions, e.g. at the Cybathlon 2020 (www.cybathlon.ethz.ch).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".