Title: OpenSim Moco: Software to optimize the motion and control of OpenSim models

Speaker: Christopher Dembia & Nick Bianco, Stanford University

Time: Thursday, November 14, 2019 10:00 am Pacific Time

Registration: https://stanford.webex.com/stanford/onstage/g.php?MTID=e8f00908ce7ccec42e40d60e8fdb04bf2

ABSTRACT 

Moco is a newly released musculoskeletal simulation tool to extend the OpenSim software’s capabilities. Unlike many other such tools that can only track observed moments, Moco allows users to customize optimization cost functions and solve a variety of problems, including:

• Motion tracking – Moco can estimate muscle forces that generated an observed motion while minimizing custom costs, including joint reaction loads.
• Motion prediction – Moco can predict motions without relying on experimental data.
• Parameter optimization – Moco can optimize parameters in a model, such as the stiffness of an exoskeleton.

Moco uses the direct collocation method, which is often faster than or can handle more diverse problems than other popular methods for musculoskeletal simulation. However, direct collocation requires extensive technical expertise to implement. Moco gives researchers access to this advanced technique through a simple and intuitive interface, thereby allowing researchers to focus on their scientific questions.

In this webinar, Moco developers Christopher Dembia and Nick Bianco from Stanford University will provide a primer on the direct collocation method, introduce the features of Moco, and highlight applications of Moco.