Functional Electrical Stimulation Plus Visual Feedback Balance Training for Standing Balance Performance Among Individuals With Incomplete Spinal Cord Injury: A Case Series
Bibliographic record
Abstract
Individuals with an incomplete spinal cord injury (iSCI) are highly susceptible to falls during walking or standing. Our objective was to evaluate a therapeutic tool for standing balance that combined functional electrical stimulation, applied bilaterally to the plantarflexors and dorsiflexors, with visual feedback balance training (FES+VFBT). Five adults with iSCI completed 12 FES+VFBT sessions over 4 weeks. During the training sessions, participants completed each of the four balance exercises twice. Visual feedback of the center-of-pressure (COP) location was provided as participants completed the balance exercises and received FES to assist with performance of the exercises. A closed-loop FES system was used in which the COP was continually monitored and the level of electrical current administered was automatically adjusted. Balance abilities were assessed pre- and post- training using clinical balance scales (i.e., Berg Balance Scale, Mini-Balance Evaluation Systems Test, and Activities-specific Balance Confidence Scale) and biomechanical assessments (i.e., postural sway measures and limits of stability test during standing). User acceptability was explored through semi-structured interviews. Improvements were seen for four of the five participants on at least one of the clinical scales following completion of the training intervention. All participants showed greater maximal COP excursion area during the limits of stability test after the training intervention, whereas only one participant demonstrated a reduction in postural sway. Specific components of FES+VFBT, including the ability to safely practice challenging balance exercises, were deemed important by the participants. These results suggest that FES+VFBT has potential as an intervention for standing balance after 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".