Neuromechanical control of the forearm muscles during gripping with sudden flexion and extension wrist perturbations
Bibliographic record
Abstract
The purpose of this study was to investigate how gripping modulates forearm muscle co-contraction prior to and during sudden wrist perturbations. Ten males performed a sub-maximal gripping task (no grip, 5% and 10% of maximum) while a perturbation forced wrist flexion or extension. Wrist joint angles and activity from 11 muscles were used to determine forearm co-contraction and muscle contributions to wrist joint stiffness. Co-contraction increased in all pairs as grip force increased (from no grip to 10% grip), corresponding to a 36% increase in overall wrist joint stiffness. Inclusion of individual muscle contributions to wrist joint stiffness enhanced the understanding of forearm co-contraction. The extensor carpi radialis longus (ECRL) and brevis had the largest stiffness contributions (34.5 ± 1.3% and 20.5 ± 2.3%, respectively), yet muscle pairs including ECRL produced the lowest co-contraction. The muscles contributing most to wrist stiffness were consistent across conditions (ECRL for extensors; Flexor Digitorum Superficialis for flexors), suggesting enhanced contributions rather than muscular redistribution. This work provides investigation of the neuromuscular response to wrist perturbations and gripping demands by considering both co-contraction and muscle contributions to joint stiffness. Individual muscle stiffness contributions can be used to enhance the understanding of forearm muscle control during complex tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".