Biomechanics of the Human Knee Joint in Maximum Voluntary Isometric Flexion: Study of Changes in Applied Moment, Agonist–Antagonist Participations, Joint Center, and Flexion Angle
Bibliographic record
Abstract
Estimation of the knee joint strength by maximum voluntary isometric contraction (MVIC) is a common practice to assess strength, coordination, safety to return to work or engage in sports after an injury, and to evaluate the efficacy of treatment modalities and rehabilitation strategies. In this study, we utilize a previously validated coupled finite element-musculoskeletal model of the lower extremity to explore the sensitivity of output measures (posterior cruciate ligament [PCL]/muscle/contact forces and passive moments) in knee MVIC flexion exercises at seated position. To do so, at three knee flexion angles (KFA), input measures (resistance moment and contribution moments of quadriceps and gastrocnemii) were varied at four levels each using the Taguchi design of experiment. Our findings reveal significant increases in PCL forces with KFA (p < 0.01), net MVIC moment (p < 0.01), and resistance moment of quadriceps (p < 0.01). In contrast, they drop at larger activity in gastrocnemii (p < 0.01). Tibiofemoral (TF) contact forces increase with the net MVIC moment (p < 0.01). The passive knee flexion moment, while highly dependent on the location at which computed, also increases with the net MVIC moment (p < 0.01). Changes in KFA, MVIC moment, and proportions thereof carried by quadriceps and/or gastrocnemii substantially affect biomechanics of the joint. Compared with level walking and stair ascent, slightly larger contact forces/stresses and much greater PCL forces are computed. This study improves our understanding of the knee joint behavior during MVIC in effective evaluation and rehabilitation interventions. Besides, it emphasizes the importance of positioning the joint center in model studies.
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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 | 0.001 |
| 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".