Webinar: Muscle-Driven Simulations and Experimental Data of Cycling, Part 2 of 2 [Video]

Caitlin Clancy from Stanford University discusses best practices for, and shares lessons learned from, working with OpenSim Moco. In particular, she discusses strategies and provides examples she used to iterate and refine the Moco optimization problem and choose different settings.

This webinar is offered jointly with the Restore Center (https://restore.stanford.edu/), an NIH-funded Medical Rehabilitation Research Resource Network Center at Stanford University.

Watch the research talk:
https://youtu.be/Ua7dfsiqO1s

Learn more:
Read the associated publications:
https://doi.org/10.1038/s41598-023-47945-5
Download the data and pipeline:
https://simtk.org/projects/cycling_sim

0:00 Introduction
2:16 Validate outputs
4:12 Ensure muscle paths are realistic for the motion
5:21 Simplify the model when possible
7:02 Check for excessive passive force in the range of motion
9:27 Ensure muscles are driving the motion
12:44 Tuning the optimization objective function
19:40 Q&A
33:27 Closing