Application of Class-Based EMG Biofeedback in Three Multiple Sclerosis Patients
Bibliographic record
Abstract
Electromyography (EMG)-based biofeedback can facilitate high volumes of exercise in individuals with severe motor impairments. Biofeedback systems based on EMG classification can integrate muscle activities from multiple sensors in a way that is flexible and scalable, allowing the choice and number of trained muscles to be tailored. The classification record can also provide insights about biofeedback quality and the exercise dose. We previously developed a class-based system and tested it within one session. We now test the feasibility of using the same system to deliver a tailored six-week intervention in three participants with advanced MS symptoms. Participants attended up to three sessions every week for 30 minutes of training. For two participants, training was divided into two 15-minute blocks with separate tailored configurations. Single-session healthy reference datasets were acquired for each tailored intervention and sliding window analyses were performed on the classification records from all sessions. We thus attempted five tailored interventions. The primary success criteria were: 1) creation of accurate classification models, and 2) participant ability to successfully interact with the biofeedback during the 15- or 30-minute session. Four of the interventions were successful with typical classification accuracy above 95% and stable feedback control during all sessions. One intervention was not successful, with median accuracy of 86% and negligible feedback control in three of seven sessions. This work suggests that class-based EMG biofeedback is feasible in multiple sclerosis patients with severe impairment, and that conducting larger studies of the technology’s impact on health outcomes is a practical next step.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".