Nov 14, 2019

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

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.

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Details

Title: OpenSim Moco: Software to optimize the motion and control of OpenSim models
Speakers: Christopher Dembia & Nick Bianco, Stanford University
Time: Thursday, November 14, 2019 at 10:00 a.m. Pacific Time

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.

OpenSim is a freely available software package for modeling the musculoskeletal system. Moco extends OpenSim’s capabilities and enables users to optimize the motion and control of OpenSim models. 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.

You can learn more about Moco and download the software by visiting https://simtk.org/projects/opensim-moco.

A preprint describing Moco is available at https://www.biorxiv.org/content/10.1101/839381v1.

You can reproduce the results from the preprint using the materials available at https://github.com/stanfordnmbl/mocopaper.