# Impact of a moving-axis knee model on musculoskeletal forces during gait

Elisa Jolas, Sacha Guitteny, Nolwenn Fougeron, Rachid Assaoui, Charles

Pontonnier, Raphaël Dumas

► To cite this version:

Elisa Jolas, Sacha Guitteny, Nolwenn Fougeron, Rachid Assaoui, Charles Pontonnier, et al. Impact of a moving-axis knee model on musculoskeletal forces during gait. Multidisciplinary Biomechanics Journal, 2025, 2, pp.405-407. 10.46298/mbj.16219. hal-05191773v2

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Submitted on 23 Oct 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Impact of a moving-axis knee model on musculoskeletal forces during gait

Elisa Jolas<sup>a*</sup>, Sacha Guitteny<sup>a</sup>, Nolwenn Fougeron<sup>b</sup>, Rachid Aissaoui<sup>c</sup>, Charles Pontonnier<sup>b</sup>, Raphaël Dumas<sup>a</sup>

<sup>a</sup> Univ. Gustave-Eiffel, LBMC UMR T9406, Lyon, France

<sup>b</sup> Univ. Rennes, Inria, CNRS, IRISA - UMR 6074, Rennes, France

<sup>c</sup> ÉTS, LIO-TS, Montréal, Canada

* Corresponding author: elisa.jolas@univ-eiffel.fr

Received date: 05/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

**Keywords:** musculoskeletal modelling, kinematics, Anybody, contact forces

© 2025 The Authors

Licence CC-BY 4.0

Published by Société de Biomécanique

## 1. Introduction

Knee osteoarthritis is a leading cause of disability worldwide. Although the link between knee loading and osteoarthritis progression remains debated, reducing knee contact forces (KCF) through conservative treatments and orthopaedic devices (e.g., knee braces, foot orthoses) is commonly recommended and may alleviate symptoms. Accurate KCF estimation is therefore crucial for treatment development. Musculoskeletal modelling is a useful tool to assess pathological muscle and joint function. In musculoskeletal modelling studies, the knee is often simplified as a hinge joint with a fixed position and orientation. However, knee kinematics is more complex, involving displacements and rotations along all three axes. Neglecting this complexity may introduce biases in the estimation of musculoskeletal loads through inverse dynamics. This study aims to develop an updated tibiofemoral hinge model with a moving-axis (Dzialo et al., 2018) to better model knee kinematics. This updated model will be compared to a fixed-axis hinge model in terms of knee moments, KCF, and musculo-tendon forces, computed using TLEM 2.0 (Carbone et al., 2015).

## 2. Methods

### 2.1 Experimental data

This study is based on a published dataset involving 50 healthy participants (38±14y.o., 1.74±0.09m, 71.6±12.3kg, 27 men/23 women; Schreiber &

Moissenet, 2019). Participants' gait at overground self-selected speed was recorded using a motion capture system (100Hz, OQUS4, Qualisys, Sweden) and two force plates (1500 Hz, OR6-5, AMTI, USA). A total of 229 stance phases were analyzed.

### 2.2 Musculoskeletal modelling

For each participant, a generic musculoskeletal model based on TLEM 2.0 was uniformly scaled to individual body height and weight using the AnyBody Modeling System (AnyBody Technology, Denmark), without incorporating additional sex- or morphology-specific characteristics. The tibiofemoral hinge model was defined using either a fixed axis between the femoral epicondyles or a moving axis, adapted from Dzialo et al. (2018). To generate the moving-axis, the relative position and orientation of the TLEM 2.0 tibia and femur meshes were reconstructed as a function of the tibiofemoral flexion angle by maximizing joint congruence through contact pressure minimization (Conconi, Sancisi, & Parenti-Castelli, 2021). Based on this reconstruction, the trajectories of the facet centers were computed as the centers of spheres fitted to the contact surfaces between the tibia and the medial and lateral femoral condyles, from -5° to 120° of tibiofemoral flexion. The moving axis was then defined in the model as the line connecting the medial and lateral facet centers. Inverse kinematics and inverse dynamics analyses were performed on the cohort using both fixed- and moving-axis hinge models.
## 2.3 Data Processing and statistical analysis

Normalized and averaged knee moments, KCF, and musculo-tendon forces across the entire cohort were compared between the two hinge models over the stance phase using Statistical Parametric Mapping (SPM) paired t-tests.

## 3. Results and discussion

The mean lower limb marker residuals were 2.2±0.3 cm for both models, indicating that the moving-axis hinge model does not affect marker tracking effectiveness. The first and second peaks in KCF along the sup/inf axis occurred, on average, at 25% and 79% of the stance phase. The moving-axis hinge model increased KCF magnitude at the first peak across all three axes (+18.6% in sup/inf axis, p<0.05, Fig. 1) compared to the fixed-axis hinge model, due to greater vastii (lateral, intermedius and medial) and hamstring forces. In contrast, KCF decreased at the second peak (-15.9% for sup/inf axis, p<0.05) due to reduced medial gastrocnemius force. These differences in contact and musculo-tendon forces resulted from variations in intersegmental knee moments: the moving-axis model showed higher flexion moments (+94.6%) and lower adduction moments (-13.9%) at the first peak, and lower internal rotation moments (-20.8%) at the second. These changes stem from a shift in the knee joint frame relative to the ground reaction force vector. The impact of the knee model used for the inverse kinematics step on the computed intersegmental moments has been reported before (Pomarat et al., 2023) and seems a crucial factor in musculoskeletal modelling.

## 4. Conclusions

Modelling the knee with a moving-axis shifted its position relative to the ground reaction force vector, influencing intersegmental moments, musculo-tendon forces, and KCF. Limitations include the assumption that kinematics, i.e. hinge axis position(s) and orientation(s), remains unchanged despite external loads and the lack of quantitative validation for KCF estimation and qualitative validation of musculo-tendon force predictions against electromyography data. Previously, only kinematics was assessed for a similar moving-axis linear model, showing 2.8 mm accuracy for translations and 1.2° for rotations during quasi-static lunges (Dzialo et al., 2018). Although this model requires further investigation and validation concerning its influence on the

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Comparison of knee contact forces (KCF), knee-spanning muscle forces, and knee moments between the fixed-axis and moving-axis models (paired t-test results). Red dotted lines indicate the average timing of the first and second vertical force peaks between the tibiofemoral models.</figcaption></figure>

aforementioned quantities, a moving-axis hinge model based on TLEM 2.0 joint kinematics could improve the quantification of joint function. Future studies should apply this model to populations with musculoskeletal disorders, incorporating personalized kinematics to reflect clinical specifics.

## Acknowledgements

We thank Michele Conconi from the University of Bologna for providing the joint kinematics data of the TLEM 2.0.

## Conflict of Interest Statement

None.

## Contributor Roles

EJ: Conceptualization, Formal analysis, Methodology, Software, Visualization, Writing—original draft; SG: Conceptualization, Formal analysis, Methodology, Supervision, Writing—review & editing; NF: Supervision, Writing—review & editing; RA: Conceptualization, Methodology, Supervision, Writing—review & editing;
CP: Conceptualization, Methodology, Supervision, Writing-review & editing; RD: Conceptualization, Formal analysis, Methodology, Supervision, Writing-review & editing.

## Data, software, code availability

The published dataset used in this study is available via this link: Schreiber & Moissenet, 2019 - https:// figshare.com/articles/dataset/A_multimodal_dataset_ of_human_gait_at

## References

Carbone, V., Fluit, R., Pellikaan, P., Krogt, M. M. van der, Janssen, D., Damsgaard, M., ... Verdonschot, N. (2015). TLEM 2.0 – A comprehensive musculoskeletal geometry dataset for subject-specific modeling of lower extremity. *Journal of Biomechanics*, 48(5), 734–741.
https://doi.org/10.1016/j.jbiomech.2014.12.034

Conconi, M., Sancisi, N., & Parenti-Castelli, V. (2021). Prediction of individual knee kinematics from an MRI representation of the articular surfaces. *IEEE Transactions on Biomedical Engineering*, 68(3), 1084–1092.
https://doi.org/10.1109/TBME.2020.3018113

Dzialo, C. M., Pedersen, P. H., Simonsen, C. W., Jensen, K. K., De Zee, M., & Andersen, M. S. (2018). Development and validation of a subject-specific moving-axis tibiofemoral joint model using MRI and EOS imaging during a quasi-static lunge. *Journal of Biomechanics*, 72, 71–80.
https://doi.org/10.1016/j.jbiomech.2018.02.032

Pomarat, Z., Guitteny, S., Dumas, R., & Muller, A. (2023). Kinetics influence of multibody kinematics optimisation for soft tissue artefact compensation. *Journal of Biomechanics*, 150, 111514.
https://doi.org/10.1016/j.jbiomech.2023.111514

Schreiber, C., & Moissenet, F. (2019). A multimodal dataset of human gait at different walking speeds established on injury-free adult participants. *Scientific Data*, 6(1), 111.
https://doi.org/10.1038/s41597-019-0124-4