Gait-Triggered Neuromuscular Electrical Stimulation with Unloader Knee Braces: A Feasibility Study
Bibliographic record
Abstract
Objective. Knee osteoarthritis (OA) commonly involves the medial compartment of the joint. Valgus unloader braces can reduce joint loading in this region but may also suppress quadriceps activation. Neuromuscular electrical stimulation (NMES) can strengthen quadriceps when voluntary activation is limited. We evaluated the feasibility of a gait-triggered NMES system used in combination with a valgus unloader brace. Methods. A portable module was developed incorporating a commercial NMES stimulator, a microcontroller, and an inertial measurement unit (IMU). Gait events (mid-swing, initial contact, and toe-off) were detected in real time to trigger quadriceps stimulation. The system was tested in eight adults with medial knee$\mathbf{O A}$and five healthy controls, each completing a$\mathbf{3 0}$-minute supervised walking session. Results. The device operated continuously throughout all sessions. OA patients walked with a significantly slower cadence than controls ($1.1 \pm 0.4$vs.$2.0 \pm 0.4$steps$\cdot \mathrm{s}^{-1}, p=0.008$). Cycle detection accuracy remained high in both groups ($91-93 \%$,$\mathbf{p}= 0.72$). No serious adverse events occurred. Conclusion. This study introduced and evaluated a novel brace-compatible, gait-triggered NMES system for knee OA. The prototype reliably synchronized stimulation with walking, was safe and represented a reproducible model for future clinical translation. Further refinements and multi-week clinical trials are warranted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".