THE INFLUENCE OF INDIVIDUAL MUSCLE GROUPS ON FEMORAL STRAIN DISTRIBUTION DURING WALKING
Bibliographic record
Abstract
INTRODUCTION Mechanical strain resulting from muscle forces in locomotion plays an important role in the maintenance of bone health [1]. Depending on their magnitude and line of action, these muscle forces may lead to an overall increase or decrease in mechanical strain [2]. Thus, the exclusion of specific muscle groups in finite element models may have a significant impact of simulation results. Our purpose was to quantify the influence of individual muscle groups on the femoral strain distribution during walking using the finite element method. METHODS Kinematic and kinetic data were collected from ten males (age 24.9 ± 4.7 yrs; height 1.7 ± 0.1 m; mass 70.1 ± 8.9 kg) walking overground at 1.25 and 1.75 m/s. Joint reaction forces and moments were calculated using standard inverse dynamics procedures. Muscle and hip contact forces were quantified using musculoskeletal modeling [3] with a static optimization routine (cost function = sum of squared muscle stresses) [4]. For both walking speeds, two instances in stance were examined in the finite element models, coinciding with the first (Peak 1) and second peak (Peak 2) of the axial hip contact force. A finite element model of a young, healthy femur was generated from the VAHKUM database ( http://www.ulb.ac.be/project/vakhum/ ) and scaled to average subject size. Bone was assigned inhomogeneous linear-elastic material properties based on apparent density [5]. The femur was physiologically constrained at the lateral epicondyle, center of the patellar groove, and femoral contact point [6]. Muscle and hip contact forces were applied as point loads. Seven different simulations were run in ABAQUS Standard v6.1 (Providence, RI) for each of the two instances in stance at both 1.25 and 1.75 m/s. For baseline analyses, all the muscle loads were included. For subsequent analyses, muscles forces from specific muscle groups (Hip Adductors, Hip Abductors, Hip Flexors, Hip Extensors, Hip Internal Rotators, and Hip External Rotators) were orderly removed. Principal strains were quantified along the anterior, lateral, medial, and posterior aspects of the femoral periosteal surface. RESULTS & DISCUSSIONS For all simulations, principal compressive and tensile strains were greatest on the medial and lateral aspect of the femur, respectively (Figure 1). Strain magnitudes for baseline analyses were consistent with in vivo measurements [7], ranging from 1,500-2,000 μe and increasing with walking speed. Strains were higher during Peak 1, compared to Peak 2, and removal of specific muscle groups had a greater influence on the strain distribution at this instance in stance. Removal of the hip extensors, abductors, and internal rotators resulted in an overall increase in the femoral strain distribution (Figure 1), suggesting these muscles have a prophylactic action to reduce femoral bending. On the other hand, removal of the hip flexors, adductors, and external rotators had a negligible effect of the femoral strain distribution (Figure 1), but these muscles were minimally activated during walking. CONCLUSIONS Specific muscle groups make important contributions to the femoral strain distribution during walking, and failure to include these muscles in finite element models will lead to erroneous conclusions regarding femoral strain magnitudes.
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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 | 0.001 | 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".