Relevance Network Modeling for Muscle Association Pattern in Reaching Movements
Bibliographic record
Abstract
Our purpose is to study how different muscles collaborate together to efficiently create a smooth, coordinated reaching movement. In the EMG literature, it has been commonplace to model the relationships between muscles using correlation and frequency-based measures such as coherence. Inspired by the observation that mutual information is a more general and reliable metric in revealing complex relationships between time series, we propose a relevance network framework for modeling temporally-aligned multi-variate sEMG recordings. Such a network can identify functional muscle associations, providing insights into the underlying motor behavior. Here we demonstrate that relevance networks can: 1) detect the effects of handedness in normal subjects, and 2) robustly detect between the healthy and stroke subjects. Specifically, the structural features of muscle associations were sensitive to handedness and disease status yet relatively robust to differences across subjects - a long-standing goal in rehabilitation research. These results warrant further study to more fully determine the extent to which the relevance networks may elucidate the complex muscle interactions in reaching movements.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".