Two models, i.e., generic-scaled and personalized, were created for each participant and used to perform MSK simulations with OpenSim 4.2 to estimate muscle and joint contact forces acting on the femur (Delp et al., 2007 (link); Steele et al., 2012 (link)). For the generic-scaled models, the generic ‘gait2392’ OpenSim model (Delp et al., 2007 (link)) with locked metatarsophalangeal joints was linearly scaled to fit to the participants’ anthropometry based on the location of surface markers (Kainz et al., 2017 (link)). For the personalized model, the Torsion Tool (Veerkamp et al., 2021 (link)) was used to modify the femoral geometry in the ‘gait2392’ model to match each child’s NSA and AVA before scaling the model. The maximum isometric muscle forces of all models were scaled based on the ratio of the body mass between the participant’s model and unscaled reference model (Eq. (1) ) (van der Krogt et al., 2016 (link); Kainz et al., 2018 (link)). In summary, we had two models for each participant which were exactly equivalent except for the femoral geometry and corresponding muscle paths and attachments of muscles.
All models and the corresponding gait analysis data were used to calculate joint angles, joint moments, muscle forces and joint contact forces using inverse kinematics, inverse dynamics, static optimization by minimizing the sum of squared muscle activations and joint reaction load analyses, respectively. Knee and ankle joint markers were only used for scaling and excluded during inverse kinematics. The remaining markers were weighted equally. Maximum marker errors and root-mean-square errors were accepted if less than 4 cm and 2 cm, respectively, as recommended by OpenSim’s best practice recommendations (Hicks et al., 2015 (link)). Additional analyses were performed to identify muscle attachments on the femur and obtain the effective directions of muscle forces (van Arkel et al., 2013 (link)). The mean waveform of the resultant HCF from all trials was calculated and the trial with the lowest root mean square difference to the mean waveform was selected as a representative loading condition. Similar to previous studies (Yadav et al., 2016 (link); Kainz et al., 2020 (link)) nine load instances were selected based on the HCF peaks and the valley in-between during the stance phase. The HCF and muscle forces acting on the femur during the nine load instances were used as loading conditions for FE analysis.
All models and the corresponding gait analysis data were used to calculate joint angles, joint moments, muscle forces and joint contact forces using inverse kinematics, inverse dynamics, static optimization by minimizing the sum of squared muscle activations and joint reaction load analyses, respectively. Knee and ankle joint markers were only used for scaling and excluded during inverse kinematics. The remaining markers were weighted equally. Maximum marker errors and root-mean-square errors were accepted if less than 4 cm and 2 cm, respectively, as recommended by OpenSim’s best practice recommendations (Hicks et al., 2015 (link)). Additional analyses were performed to identify muscle attachments on the femur and obtain the effective directions of muscle forces (van Arkel et al., 2013 (link)). The mean waveform of the resultant HCF from all trials was calculated and the trial with the lowest root mean square difference to the mean waveform was selected as a representative loading condition. Similar to previous studies (Yadav et al., 2016 (link); Kainz et al., 2020 (link)) nine load instances were selected based on the HCF peaks and the valley in-between during the stance phase. The HCF and muscle forces acting on the femur during the nine load instances were used as loading conditions for FE analysis.
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