Surface electrical stimulation for foot drop: Control aspects and walking performance
Bibliographic record
Abstract
Use of electrical stimulation to correct foot drop in hemiplegia was proposed over 40 years ago. Recently, improved control strategies have been developed and implemented in commercially available devices. In this article we review the control methods that have been used and present some results from a multi-center clinical trial. A foot-drop stimulator improves the gait pattern and results in an immediate increase in walking speed. In this sense it acts like an ankle-foot orthosis and this immediate increase will be referred to as an orthotic effect. Prolonged use of a foot drop stimulator over a period of months results in further, large increases in walking speed both with the stimulator on and off. Evidence indicates that a part of this increase results from daily use that strengthens residual cortico-spinal connections. Therefore the improvement over time will be referred to as a therapeutic effect. We found that people with non-progressive and progressive conditions of the central nervous system have an orthotic benefit, as well as a therapeutic up to 3 months of use. In generally non-progressive conditions such as stroke, further therapeutic increases are seen up to at least 11 months of use. In disorders such as multiple sclerosis, the progression of the disease eventually overcomes the early therapeutic effects. In conclusion, many individuals can benefit from commercially available foot-drop stimulators with improved control strategies and cosmetic design.
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 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 | 0.001 |
| 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) | 0.002 | 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".