Combining robotics and functional electrical stimulation for assist-as-needed support of leg movements in stroke patients: A feasibility study
Bibliographic record
Abstract
PURPOSE: Rehabilitation technology can be used to provide intensive training in the early phases after stroke. The current study aims to assess the feasibility of combining robotics and functional electrical stimulation (FES), with an assist-as-needed approach to support actively-initiated leg movements in (sub-)acute stroke patients. METHOD: Nine subacute stroke patients performed repetitions of ankle dorsiflexion and/or knee extension movements, with and without assistance. The assist-as-needed algorithm determined the amount and type of support needed per repetition. The number of repetitions and range of motion with and without assistance were compared with descriptive statistics. Fatigue scores were obtained using the visual analogue scale (score 0-10). RESULTS: Support was required in 44 % of the repetitions for ankle dorsiflexion and in 5 % of the repetitions of knee extension, The median fatigue score was 2.0 (IQR: 0.2) and 4.0 (IQR: 1.5) for knee and ankle, respectively, indicating mild to moderate perceived fatigue. CONCLUSION: This study demonstrated the feasibility of assist-as-needed assistance through combined robotic and FES support of leg movements in stroke patients. It proved particularly useful for ankle dorsiflexion. Future research should focus on implementing this approach in a clinical setting, to assess clinical applicability and potential effects on leg function.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".