Muscle Coordination Patterns for Efficient Cycling
Bibliographic record
Abstract
INTRODUCTION/PURPOSE: Cycling is a repetitive activity using coordinated muscle recruitment patterns to apply force to the pedals. With more muscles available for activation than required, some patterns produce high power, whereas some are more efficient. The purpose of this study was to identify relationships between muscle coordination and factors affecting muscle coordination to explain changes in overall mechanical efficiency (ηO). METHODS: Surface EMG, kinematics, and pedal forces were measured at 25%, 40%, 55%, 60%, 75%, and 90% V˙O(2max). Principal component analysis was used to establish muscle coordination, kinematic, and pedal force patterns associated with high and low ηO. RESULTS: At 55%-60% V˙O(2max), ηO was maximized and was highly related to the muscle coordination patterns. At high ηO, there was more medial and lateral gastrocnemii and soleus; less gluteus maximus, rectus femoris, and tibialis anterior; later medial and lateral vastii and biceps femoris; and earlier semitendinosus muscle activity resulting in an even distribution and synchronization of peak activity. Also, the ankle was more plantar flexed through the top and downstroke of the pedal cycle and more dorsiflexed during the upstroke for high ηO. The ηO was independent of the pedal force application. CONCLUSIONS: The results indicate that increased ηO is achieved through the coordination of muscles crossing the same joint, sequential peak activation from knee to hip to ankle, and reliance on multiple muscles for large joint torques. Also, muscle activity variability across the top and bottom of the cycle indicates that left and right leg muscle coordination may play a significant role in efficient cycling. These findings imply that cycling at 55%-60% V˙O(2max) will maximize the rider's exposure to high efficient muscle coordination and kinematics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".