Development of a closed-loop controller for functional electrical stimulation therapy plus visual feedback balance training for standing balance training
Bibliographic record
Abstract
Individuals with incomplete spinal cord injury (iSCI) demonstrate impaired upright balance, resulting in increased fall risk. Task-specific visual feedback balance training (VFBT) has previously been shown to improve upright balance. In addition, therapies using functional electrical stimulation (FES) have been shown to improve various motor functions. Combining VFBT with FES therapy (FES+VFBT) may synergistically improve balance control for those with iSCI. Here we developed the FES+VFBT system that delivered physiologically relevant electrical stimulations to soleus (SOL) and tibialis anterior (TA) muscles during VFBT. Ten young able-bodied individuals participated. Kinematic, kinetic, SOL and TA electromyography (EMG) data during quiet standing and limits-of-stability test were used to design the controller for the FES+VFBT system. To evaluate the performance of the designed controller, the controller outputs, which represented stimulation intensities, were compared with the recorded SOL and TA EMG during the four tasks associated with VFBT (i.e., bullseye, hunting, colour-matching, and ellipse tasks). Except for the bullseye task, the designed controller outputs were highly correlated with the recorded EMG, suggesting that the controller could generate electrical stimulations in a physiological manner. We expect that the addition of FES therapy to VFBT could contribute to improving standing balance for individuals with iSCI.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".