[Opensim-announcement] Webinar: Personalizing an Upper Body Model, Virtual Office Hours, and more

[Matthew Petrucci]

Webinar on Personalizing a Computational Upper Body Model

Wednesday, September 11, 2024, 9:00 AM PDT


We are pleased to announce our upcoming webinar with Jennifer Maier from Stanford University entitled “Personalizing a Computational Upper Body Model.” Musculoskeletal models of the shoulder are needed to understand the mechanics of overhead motions, but existing models are generic and might not accurately represent an individual’s scapular kinematics. Dr. Maier will demonstrate her shoulder rhythm personalization approach to scaling a musculoskeletal model to produce less error in the kinematic analysis of shoulder motions. She will also guide the audience through a step-by-step tutorial for model scaling and personalization. This event will be hosted jointly by the Mobilize<http://mobilize.stanford.edu> and Restore<http://restore.stanford.edu/> Centers on Wednesday, September 11, 2024, at 9:00 AM Pacific Time. Learn more and register<https://mobilize.stanford.edu/webinar-personalizing-a-computational-upper-body-model/> | Read the preprint<https://www.biorxiv.org/content/10.1101/2024.06.12.598739v1>



Apply to Participate in Virtual Office Hours for Biomechanical Modeling or Machine Learning Research Questions

Application Deadline: September 30, 2024, 5 PM local time

Office Hour Dates: October 21-25, 2024


The Mobilize<http://mobilize.stanford.edu> and Restore<http://restore.stanford.edu/> Centers are pleased to announce they will be holding Virtual Office Hours  October 21-25, 2024, to support researchers working with wearable sensors, video technology, and other modalities in rehabilitation research. We will have three tracks: 1) OpenCap<http://opencap.ai/> or other video-based analysis of movement, 2) OpenSim<https://opensim.stanford.edu/> or other questions about biomechanical modeling, including via IMUs, and 3) Machine learning with time series or other movement/rehab data. All phases of a research project are supported, such as formulating a research question, choosing and planning appropriate methods, and addressing issues with carrying out the study. We welcome and encourage individuals to apply as a team, if relevant. Learn more and apply<https://restore.stanford.edu/2024/08/20/apply-to-participate-in-virtual-office-hours-for-biomechanical-modeling-or-machine-learning-research-questions-10/>





OpenSim 4.5.1 API is Released!


We are pleased to announce the release of OpenSim 4.5.1. This is an API-only release (i.e., no updates to the application), but contains many useful software updates including:

  *   The main OpenSim Conda packages now include all Moco features (i.e., no need to use “opensim-moco” packages)
  *   New Conda packages for Macs with M1 chips or later
  *   SynergyController, a new controller class to model muscle synergies in OpenSim
  *   Performance improvements to speed-up muscle path calculations when using function-based muscle paths
  *   New Moco functionality to calculate and track joint moments and to include controllers, like the new synergy controller, in optimization problems


Install the new Python packages - OpenSim Confluence: Scripting in Python<https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python>

View the Doxygen documentation - OpenSim 4.5.1 API docs<https://simtk.org/api_docs/opensim/api_docs451/>

View the change logs for OpenSim<https://github.com/opensim-org/opensim-core/blob/main/CHANGELOG.md> and Moco<https://github.com/opensim-org/opensim-core/blob/main/CHANGELOG_MOCO.md>





MR3 Network 4th Annual Scientific Retreat - Catalyzing Transdisciplinary Research to Develop Innovative Rehabilitation Approaches with Lasting Impacts


The Medical Rehabilitation Research Resource (MR3) Network is pleased to announce its 4th scientific retreat to be held virtually September 19-20, 2024. The goal of the retreat is to change the rehabilitation research landscape by developing novel transdisciplinary strategies to sustain and improve function once individuals return to their lived environments. Transdisciplinary team members may include, for example, engineers, behavioralists, implementation scientists, data scientists, persons with lived experience, tele-interventionalists, and more. Learn more and register<https://ncmrr.org/education-training/2024-retreat>






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