Oct 30, 2014

Webinar: OpenSim for Open-Source Musculoskeletal Modeling and Simulation: Roadmap for the Future

OpenSim's R&D Manager Jen Hicks presents future plans for the project


A recording of the event is available for viewing.


Title: OpenSim for Open-Source Musculoskeletal Modeling and Simulation: Roadmap for the Future
Speaker: Dr. Jen Hicks, Stanford University
Time: Thursday, October 30, 2014 at 10:00 a.m. Pacific Daylight Time



This webinar will provide an overview of the progress the OpenSim project has made over the past decade and the challenges we plan to tackle in the coming years. This talk will be a re-broadcast of our presentation at the World Congress of Biomechanics for those who didn't make it to Boston . . . or who couldn't fit in the packed room.

More Details

OpenSim is an open source software platform for modeling the neuromusculoskeletal system and simulating muscle-driven motions. To date we have released ten versions of OpenSim, downloaded by 14,000 unique users worldwide and cited by over 500 papers and abstracts. Recent advances include an interface for developers to create new model components (e.g., muscle models), live model-editing in the OpenSim application, and a MATLAB scripting interface to allow users to extend OpenSim's functionality (e.g., writing a new analysis).

There are several challenges and opportunities for the future. Our development team must continue to improve the software and online infrastructure to make it even easier for the OpenSim community to share models, algorithms, and data. Several teams are working to translate OpenSim to clinical settings, but more work is needed to create user-friendly pipelines and establish OpenSim's utility in treatment planning and evaluation. Modeling neural control remains a grand challenge in the field of computational biomechanics, and OpenSim must continue to develop an infrastructure that makes it easy for researchers with a wide variety of backgrounds to develop and test new control methods. Finally, new techniques and data for validation are needed as the applications of musculoskeletal simulation continue to expand.

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Webinar: Robust control strategies for musculoskeletal models using deep reinforcement learning

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