A small question about the difference of the scaling method #25
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And as the introduction in the SimTK web that "This project presents an optimization based technique to adjust muscle parameters of models linearly scaled from a generic one and to obtain reasonable estimation of optimal fiber lengths and tendon slack lengths in models generated from medical images." So, does it mean that if I want to use this method for muscle length scaling, I need to collect the medical images data, only motion data or the static pose data is definitely not enough if I use your algorithm? |
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Hi Yingfan, The scaling methods for muscle length scaling in your method and the method used in OpenSim are totally different and your method could be used as a plug-in in OpenSim. From the OpenSim scaling tutorial, I could do the scaling with static pose data. For your method, could I also only use the static pose data or I need to collect some motion data for muscle length scaling? Does it mean that if I want to use this method for muscle length scaling, I need to collect the medical images data, only motion data or the static pose data is definitely not enough if I use your algorithm? I hope this helps! Luca |
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Hi,
I am a rookie in human modeling research. First, really appreciate that you created and open-sourced such a great tool for muscle parameters scaling for the OpenSim model!
I just have a small question and I am not sure whether I understand it correctly. So, the scaling method proposed in this tool for muscle optimal fiber length and tendon slack length scaling is totally different from that implemented in OpenSim?
Because as introduced in OpenSim scaling tutorial that a muscle’s new optimal_fiber_length and tendon_slack_length are computed during the scaling proces with a scaling factor, I guess the scaling factor is calculated internally based on the collected pose data.
So, am I correct that
Sorry to bother you that maybe my questions sound a bit weird. Really appreciate if you could share any insights regarding these questions.
Wishing you all the best.
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