Perturbing postural stability during treadmill walking with dysfunctional electrical stimulation
Bibliographic record
Abstract
Abstract Functional electrical stimulation is commonly used to enhance human movement through low-level electrical activation of muscles. More recently, dysfunctional electrical stimulation (DFES) has been proposed as a method to perturb gait by artificially inducing discomfort and mimicking inadequate muscle activity. Here we investigated strategies to induce internal perturbations during treadmill walking using DFES by systematically varying the timing and target muscle. Eleven healthy participants walked at three different speeds while DFES was applied to the tibialis anterior (TA), soleus (SOL), rectus femoris (RF), and biceps femoris (BF) muscles at 25%, 50%, 75%, and 100% of the gait cycle, each for a duration of 0.2 seconds. The gait cycle was time-locked to heel contact (0%). Results showed a significant reduction in the anterior-posterior margin of stability compared to baseline, particularly when DFES was applied to the SOL at 75%, the RF at 50%, and the BF at 75% of the gait cycle. Under these conditions, increased knee flexion and shorter stride intervals were observed relative to baseline. In conclusion, we identified effective DFES conditions to induce postural instability during walking. By mimicking inadequate muscle activity, DFES provides a promising method to study dynamic balance control and mechanisms underlying falls in neurological populations.
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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 | 0.000 | 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".