The Effects of Exercise-Induced Muscle Damage on Antagonist Muscles in Upper Limb Reaching Tasks
Bibliographic record
Abstract
Coordination and control of multiple muscle groups are critical when adapting to movement disruptions, such as altered goals or pathways. Humans use sensory information from internal and external sources to engage in online movement control. Proprioception, mainly obtained from sensors in the muscles such as muscle spindles, is an important source of sensory information. After intense or unusual exercise, exercise-induced muscle damage (EIMD) can occur within 24 to 48 hours. EIMD can also increase noise in the signals generated by the sensory organs in the muscles and may influence online movement control. The objective of the study is to investigate the effects of EIMD on upper-limb movement control. Four right-handed, neurologically healthy, female participants (aged 20-21, M = 0.5) took part in the pilot experiment, they had normal, or corrected-to-normal vision, and participated in less than 5 hours of structured strength training weekly. The study consisted of familiarization and baseline testing, an exercise protocol, and a muscle damage assessment. In each session, a maximal voluntary contraction of the biceps brachii was assessed using a dynamometer. This dynamometer was also used for the EIMD-inducing protocol. To test the effects of EIMD on online control, participants completed a series of reaching tasks using the Kinarm Exoskeleton Robot. During some reaches, participants were required to correct their movement to either a force or target perturbation. Our pilot data shows that our protocol successfully induced muscle damage, as well as less antagonistic muscle (bicep) activation during reaching tasks. This suggests reduced biceps muscle contribution to movement control post-exercise. A decrease in movement time could signify learning and requires further exploration. We are hopeful that with further data collection, we will establish that participants are less accurate and have reduced flexor muscle activity in responses to both types of perturbations, compared to baseline.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".