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<p style="margin:0in"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black;background:white">Webinar on Bone and Joint Shape Modeling in Musculoskeletal Models</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p style="margin:0in"><i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black">Wednesday, October 29, 2025 at 9:00 AM PDT</span></i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
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<p style="margin:0in"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black;background:white">We are pleased to announce our upcoming webinar with Allison Clouthier, PhD from the University of Ottawa and Erin Lee, PhD from the University
of Waterloo, entitled “Integrating Bone and Joint Geometry into Musculoskeletal Models.” </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p style="margin:0in"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black;background:white"> </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p style="margin:0in"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black;background:white">Certain bone and joint shape features are linked to injury risk and disease severity, but these associations fail to explain how
<i>individual</i> bone shapes impact those conditions and their treatment. In this webinar, Dr. Lee and Dr. Clouthier will discuss how incorporating joint geometry into musculoskeletal models can reveal insights about the relationship between certain bone features and
increased injury risk. They will also lead an interactive tutorial demonstrating how to map soft tissue attachment sites across bone geometries using statistical shape models and incorporate those sites into a musculoskeletal model. This event will be hosted
jointly by the </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><a href="http://mobilize.stanford.edu"><span style="color:#1155CC;background:white">Mobilize</span></a><span style="color:black;background:white"> and
</span><a href="http://restore.stanford.edu/"><span style="color:#1155CC;background:white">Restore</span></a><span style="color:black;background:white"> Centers.
</span><a href="https://mobilize.stanford.edu/webinar-integrating-bone-and-joint-geometry-into-musculoskeletal-models/"><span style="color:#1155CC;background:white">Learn more and register</span></a><span style="color:black;background:white"> |</span><a href="https://doi.org/10.1016/j.medengphy.2019.02.009"><span style="color:black;background:white">
</span><span style="color:#1155CC;background:white">Read the paper</span></a><o:p></o:p></span></p>
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<p style="margin:0in"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black;background:white">Demo Available for GaitDynamics, a Generative Foundation Model for Analyzing Human Walking and Running</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
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<p style="margin:0in"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black;background:white">Data driven-models are a promising solution to quantify gait dynamics with less cost and time compared with traditional lab-based experiments.
Typical data-driven models are limited to a specific downstream task with fixed inputs and outputs. GaitDynamics is the first generative foundation model to quantify gait dynamics for different gait patterns. It is a flexible, scalable solution that is able
to predict both motions and forces for human walking and running with partial or no input data. Postdoctoral fellow Tian Tan and colleagues from the NIH-funded
</span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><a href="http://restore.stanford.edu"><span style="color:#1155CC;background:white">Restore</span></a><span style="color:black;background:white"> and
</span><a href="http://mobilize.stanford.edu"><span style="color:#1155CC;background:white">Mobilize</span></a><span style="color:black;background:white"> Centers and the
</span><a href="https://humanperformancealliance.org/"><span style="color:#1155CC;background:white">Wu Tsai Human Performance Alliance</span></a><span style="color:black;background:white"> developed the model and have made it open-source, now including a HuggingFace
demo to test with your own data. </span><a href="https://huggingface.co/spaces/alanttan/GaitDynamics"><span style="color:#1155CC;background:white">Try Demo</span></a><span style="color:black;background:white"> |
</span><a href="https://github.com/stanfordnmbl/GaitDynamics"><span style="color:#1155CC;background:white">Access GaitDynamics code</span></a><span style="color:black;background:white"> |
</span><a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC11957236/"><span style="color:#1155CC;background:white">Read preprint</span></a><span style="color:black;background:white"> </span><o:p></o:p></span></p>
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<p style="margin:0in;background:white"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#202020;background:white">—</span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p style="margin:0in;background:white"><i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#292A2E;background:white">OpenSim is supported by the
</span></i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black"><a href="https://mobilize.stanford.edu/"><i><span style="color:#1155CC;background:white">Mobilize Center</span></i></a></span><i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#292A2E;background:white">,
an NIH Biomedical Technology Resource Center (grant P41 EB027060); the </span></i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black"><a href="https://restore.stanford.edu/"><i><span style="color:#1155CC;background:white">Restore Center</span></i></a></span><i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#292A2E;background:white">,
an NIH-funded Medical Rehabilitation Research Resource Network Center (grant P2C HD101913); and the
</span></i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black"><a href="https://humanperformancealliance.org/"><i><span style="color:#1155CC;background:white">Wu Tsai Human Performance Alliance
</span></i></a></span><i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#292A2E;background:white">through the Joe and Clara Tsai Foundation. </span></i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
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