Upper limb and trunk muscle activity patterns during seated and standing cycling
Bibliographic record
Abstract
The objective of this study is to clarify the functional roles of upper limb muscles during standing and seated cycling when power output increases. We investigated the activity of seven upper limb and trunk muscles using surface electromyography (EMG). Power outputs ranged from ~100-700 W with a pedalling frequency of 90 revolution per minute. Three-dimensional handle and pedal forces were simultaneously recorded. Using non-negative matrix factorisation, we extracted muscle synergies and we analysed the integrated EMG and EMG temporal patterns. Most of the muscles showed tonic activity that became more phasic as power output increased. Three muscle synergies were identified, associated with (i) torso stabilisation, (ii) compensation/generation of trunk accelerations and (iii) upper body weight support. Synergies were similar for seated and standing positions (Pearson's r > 0.7), but synergy #2 (biceps brachii, deltoidus and brachioradialis) was shifted forward during the cycle (~7% of cycle). The activity levels of synergy #1 (latissimus dorsi and erector spinae) and synergy #2 increased markedly above ~500 W (i.e., ~+40-70% and +130-190%) and during periods corresponding to ipsi- and contralateral downstrokes, respectively. Our study results suggest that the upper limb and trunk muscles may play important roles in cycling when high power outputs are required.
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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 | 0.000 |
| 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".