Postoperative healing patterns in elbow using electromyography: Towards the development of a wearable mechatronic elbow brace
Bibliographic record
Abstract
Musculoskeletal (MSK) conditions are the most common cause of severe long-term pain and physical disability. Current postoperative treatment for patients requires them to follow a long-term physiotherapy program customized for each specific case; however, this process can be complex, time-consuming and without the right therapy it may end up being ineffective. A possible solution involves the development of wearable mechatronic elbow braces that use electromyography (EMG) to identify patient intent. However, EMG characteristics change based on the health of the individual and therefore require further investigation. In order to quantify the progress of MSK injury patients and assess their neuromuscular health, EMG signals from 16 healthy individuals and 15 postoperative patients were collected and analyzed. The experiments conducted show that EMG can be used as a method for assessing MSK health. A normal range across the muscle groups has been identified to which the patient population was compared. This showed statistically significant differences in the magnitudes of muscle recruitment and activation between the two groups. Furthermore, a comparison within the patient population at the beginning of their therapy versus at the end of their therapy was conducted. Statistical differences arose in this second analysis further proving that patients' signals tend to change and show trends closer to those of the healthy population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".