Bibliographic record
Abstract
In this study, we examined the EMG of six forearm muscles to determine the influence of posture and force level on muscle contribution during grasp. To evaluate the control of the mechanically redundant forearm muscle system, the cross-correlation function was used. The cross-correlation function has been used to assess common EMG signal between neighboring muscles and discussed in terms of cross-talk and 'common neural drive' to muscles. Both of these concepts are important in the forearm. To examine muscle contributions to grip under various posture conditions, we examined the power grasp using different force levels as well as changing forearm and wrist posture. Using a grip dynamometer, ten participants generated 5% and 50% maximal grips in each combination of three forearm (pronated, neutral, supinated) and three wrist postures (flexed, neutral, extended). For each trial, force was linearly increased from rest to the desired level and held for three seconds. To examine common function (or common neural drive) between muscles, cross-correlation functions were calculated from the raw EMG between each of the six muscles monitored. The muscles included finger flexors (FDS), radial and ulnar wrist flexors (FCR, FCU), common finger extensor (EDC), and the radial and ulnar wrist extensors (ECR, ECU). Preliminary analysis was based on three of the ten participants. As expected, the size of the cross-correlation depended on the muscles being compared, with correlations within the flexor or extensor groups being greater than any correlations between the groups (p < 0.0001). Correlations also tended to increase with increasing effort level (p < 0.02). This was especially true for the finger flexors versus either wrist flexor but not within the extensors. Within each effort level, wrist angle altered the magnitude of the cross-correlation. We chose to evaluate muscle activity and control in the forearm using the cross-correlation function on raw EMG as approaches using average or smoothed EMG appear to overestimate common activity. Further analysis of the remaining seven participants' data will further reveal the control mechanisms of the forearm muscles with changes in posture and force.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".