Closed-loop Control of Peripheral Nerve Stimulation using Resource-efficient Neural Networks
Bibliographic record
Abstract
Closed-loop control of functional electrical stimulation (FES) can restore movement to paralyzed limbs by using recorded nerve signals to modulate stimulation in real-time. Previous work performed state-of-the-art classification of nerve signals using neural networks (NNs). This thesis explored reduced NNs for use in implanted systems and incorporated such NNs into closed-loop stimulation. NNs of varying complexities were evaluated for performance and resource usage when classifying individual naturally-evoked compound action potentials, previously collected from rat sciatic nerves using 7x8-channel cuff electrodes. Acute in vivo experiments used NNs to estimate nerve branch activity in real-time as feedback for neural stimulation, producing desired ankle movement trajectories. Reduced NNs achieved macro F1-scores of 0.70-0.71 while remaining within resource constraints defined by NN implementations in integrated circuit fabrication technologies. We demonstrated closed-loop stimulation in 6 rats. NNs that classify nerve signals can be reduced to fit implantable hardware platforms and used for closed-loop stimulation control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".