Modular transcutaneous functional electrical stimulation system
Bibliographic record
Abstract
A new multipurpose programmable transcutaneous electric stimulator, Compex Motion, was developed to allow users to design various custom-made neuroprostheses, neurological assessment devices, muscle exercise systems, and experimental setups for physiological studies. Compex Motion can generate any arbitrary stimulation sequence, which can be controlled or regulated in real-time using any external sensor or laboratory equipment. Compex Motion originated from the existing Compex 2 electric stimulator, manufactured by a Swiss based company, Compex SA. The Compex Motion stimulator represents a further evolution and expansion of the ETHZ-ParaCare functional electrical stimulation system. This stimulator provides all the advanced functional electrical stimulation (FES) application and control features and can be easily incorporated into any standard rehabilitation program. Compex Motion has successfully been applied as a neuroprosthesis for walking, reaching and grasping in more than 100 stroke and spinal cord injured patients. This system has also been used to strengthen muscles and to investigate muscle properties in able-bodied subjects. Compex Motion is a multipurpose FES system specially designed to promote sharing and exchanging of stimulation protocols, sensors, and user interfaces. To the best of our knowledge an FES system that has similar capabilities does not exist yet.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".