BIONic WalkAide for correcting foot drop
Bibliographic record
Abstract
The goal of this study was to test the feasibility and efficacy of using microstimulators (BIONs) to correct foot drop, the first human application of BIONs in functional electrical stimulation (FES). A prototype BIONic foot drop stimulator was developed by modifying a WalkAide2 stimulator to control BION stimulation of the ankle dorsiflexor muscles. BION stimulation was compared with surface stimulation of the common peroneal nerve provided by a normal WalkAide2 foot drop stimulator. Compared to surface stimulation, we found that BION stimulation of the deep peroneal nerve produces a more balanced ankle flexion movement without everting the foot. A 3-D motion analysis was performed to measure the ankle and foot kinematics with and without stimulation. Without stimulation, the toe on the affected leg drags across the ground. The BIONic WalkAide elevates the foot such that the toe clears the ground by 3 cm, which is equivalent to the toe clearance in the unaffected leg. The physiological cost index (PCI) was used to measure effort during walking. The PCI is high without stimulation (2.29 +/- 0.37; mean +/- S.D.) and greatly reduced with surface (1.29 +/- 0.10) and BION stimulation (1.46 +/- 0.24). Also, walking speed is increased from 9.4 +/- 0.4 m/min. without stimulation to 19.6 +/- 2.0 m/min. with surface and 17.8 +/- 0.7 m/min. with BION stimulation. We conclude that functional electrical stimulation with BIONs is a practical alternative to surface stimulation and provides more selective control of muscle activation.
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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) | 0.000 | 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".