Walking and Running Require Greater Effort from the Ankle than the Knee Extensor Muscles
Bibliographic record
Abstract
INTRODUCTION: The knee and ankle extensors as human primary antigravity muscle groups are of utmost importance in a wide range of locomotor activities. Yet, we know surprisingly little about how these muscle groups work, and specifically, how close to their maximal capacities they function across different modes and intensity of locomotion. Therefore, to advance our understanding of locomotor constraints, we determined and compared relative operating efforts of the knee and ankle extensors during walking, running, and sprinting. METHODS: Using an inverse dynamics biomechanical analysis, the muscle forces of the knee and ankle extensors during walking (1.6 m·s), running (4.1 m·s), and sprinting (9.3 m·s) were quantified and then related to maximum forces of the same muscle groups obtained from a reference hopping test that permitted natural elastic limb behavior. RESULTS: During walking, the relative effort of the ankle extensors was almost two times greater compared with the knee extensors (35% ± 6% vs 19% ± 5%, P < 0.001). Changing walking to running decreased the difference in the relative effort between the extensor muscle groups, but still, the ankle extensors operated at a 25% greater level than the knee extensors (84% ± 12% vs 63% ± 17%, P < 0.05). At top speed sprinting, the ankle extensors reached their maximum operating level, whereas the knee extensors still worked well below their limits, showing a 25% lower relative effort compared with the ankle extensors (96% ± 11% vs 72% ± 19%, P < 0.01). CONCLUSIONS: Regardless of the mode of locomotion, humans operate at a much greater relative effort at the ankle than knee extensor muscles. As a consequence, the great demand on ankle extensors may be a key biomechanical factor limiting our locomotor ability and influencing the way we locomote and adapt to accommodate compromised neuromuscular system function.
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) | 0.003 | 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".