Gait event discrimination using ALNs for control of FES in foot-drop problem
Bibliographic record
Abstract
Discrimination of stance and swing phases of the gait is required for control of functional electrical stimulation (FES) used to assist with ankle dorsiflexion in foot-drop problem. Simple thresholds applied to a human whole nerve signal processed using a sophisticated digital signal processing technique did not result in a safe and reliable control method. In this preliminary study, the authors use the same sensory signals to evaluate a gait event discriminator (GED), based on Adaptive Logic Networks (ALNs). The evaluation was performed off-line using neural signals for sensory feedback and a signal from a heel switch as the output to the stimulator. The neural signal was recorded using a cuff electrode implanted around the calcaneal nerve in the left leg of a male subject and the heel switch was installed inside the shoe of the same leg. Preliminary results suggest that ALNs can discriminate precise timing of heel contact and heel lift during FES-assisted walking. Restriction rules based on a priori knowledge were used to verify decisions made by ALNs and to eliminate infrequent functional errors providing maximum safety for the subject.
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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 | 0.002 |
| 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) | 0.001 | 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".