Join members of the OpenSim Team on Wednesday July 26th for an introduction to OpenSim and new tools for rapidly developing musculoskeletal simulations.

New tools for rapidly developing musculoskeletal simulations are enabling more researchers to leverage simulations by reducing the barrier to entry and the burden of processing data. In this workshop, we will present new web-based tools that automatically calculate skeletal kinematics from video (OpenCap) or marker data (AddBiomechanics), and that can be combined with direct collocation methods to generate muscle-driven simulations. Participants will go “under the hood” with a combination of didactic portions and hands-on examples so that they can leverage and extend these open-source tools.

Please follow the instructions below to prepare for the workshop.   

Install Anaconda

Anaconda is a Python distribution that includes package and environment management. It is required for the OpenCap portion of the workshop, and optional for the AddBiomechanics portion. If using Python for both, we will use Anaconda to create separate environments for the OpenCap and AddBiomechanics examples.

1. Download and install Anaconda.

Part 1: OpenCap

1. Create account

1. Create an OpenCap account: https://www.app.opencap.ai/.

2. Install requirements

The second demo of the OpenCap portion of the workshop may be completed in Windows, Mac, or Linux. It will require cloning/downloading the opencap-processing Github repository and setting up a Python environment using Anaconda. Follow the install instructions here or perform the following steps:

1. Open the Anaconda Prompt.

2. Create environment (python 3.9 recommended)

   (base) C:\Users\me> conda create -n opencap-processing python=3.9

3. Activate environment

   (base) C:\Users\me> conda activate opencap-processing

4. Install OpenSim within environment

   (opencap-processing) C:\Users\me> conda install -c opensim-org opensim=4.4=py39np120

5. (Optional): Install an IDE such as Spyder

   (opencap-processing) C:\Users\me> conda install spyder

6. Clone/download the repository to your machine
   1. Using Git

      1. Navigate to the directory where you want to download the code. For example:

         (opencap-processing) C:\Users\me> cd Documents

      2. Clone the repository

         (opencap-processing) C:\Users\me\Documents> git clone https://github.com/stanfordnmbl/opencap-processing.git

   2. Without using Git
      
      1. Download the repository by visiting https://github.com/stanfordnmbl/opencap-processing/archive/refs/heads/main.zip
      2. Unzip the folder
         • We will assume your PATH looks like C:\Users\me\Documents\opencap-processing and that the path to the README file is C:\Users\me\Documents\opencap-processing\README.md
7. Install required Python packages

   1. Navigate to the repository (make sure you are in Documents)

      (opencap-processing) C:\Users\me\Documents> cd opencap-processing

   2. Install packages

      (opencap-processing) C:\Users\me\Documents\opencap-processing> python -m pip install -r requirements.txt

8. Create environment variable for authentication, you will be prompted to provide your OpenCap credentials. An environment variable (.env file) will be saved after authenticating.

   (opencap-processing) C:\Users\me\Documents\opencap-processing> python createAuthenticationEnvFile.py

9. Run example.py to verify that everything was correctly installed so far. If you get some plots popping out, then you can proceed to step 10.

   (opencap-processing) C:\Users\me\Documents\opencap-processing> python createAuthenticationEnvFile.py

10. If you're a Mac user, you can stop here. If you're on Windows or Linux, you'll need this extra step. This part of the workshop will involve running dynamic simulations of walking using OpenSimAD, which is a custom version of OpenSim with support for Automatic Differentiation (AD). OpenSimAD requires a compiler, which is not installed by default on Windows, and the OpenBLAS libraries, which you need to manually install on Linux.

    1. (Windows only): Install Visual Studio

       1. The Community variant is sufficient and is free for everyone.

       2. During the installation, select the workload Desktop Development with C++.

       3. The code was tested with the 2017, 2019, and 2022 Community editions.

    2. (Linux only): Install OpenBLAS libraries

       sudo apt-get install libopenblas-base

11. Run Examples/example_walking_opensimAD.py to verify that everything was correctly installed so far. If you see some IPOPT iterations, then you're all set. We'll go through the example during the workshop.

    (opencap-processing) C:\Users\me\Documents\opencap-processing> cd Examples 
    (opencap-processing) C:\Users\me\Documents\opencap-processing\Examples> example_walking_opensimAD.py

Part 2: AddBiomechanics

1. Create account

1. Create an AddBiomechanics account: https://addbiomechanics.org/.

2. Software requirements

The AddBiomechanics portion of the workshop may be completed in either Windows or Mac. On Mac, OpenSim scripting examples may only be performed in Matlab. Windows users may use Matlab or a Python conda environment for the scripting examples.

• Windows 10 or later (64-bit) or Mac (macOS 11 or later)
• Python via Anaconda
  • Python 3.10
  • Matplotlib
• Matlab
  • Matlab 2016b or later (64-bit). Matlab 2018a or later is best.
• A text editor (e.g., Notepad++)

3. OpenSim installation

OpenSim with Python (Windows only)

1. Open the Anaconda Prompt.

2. Create a new conda environment for Python 3.10.

   (base) C:\Users\me> conda create -n addbio-processing python=3.10 matplotlib 

3. Activate the conda environment.

   (base) C:\Users\me> conda activate tgcs2023

4. If paths to previous OpenSim versions exist on your PYTHONPATH environment variable, remove them and restart the shell.

5. Install the OpenSim conda package. This installs a version of OpenSim including Moco that is compatible with Python 3.10 and includes NumPy 1.21. 

   (addbio-processing) C:\Users\me> conda install -c opensim-org opensim-moco=4.4.1=py310np121

6. Launch a Python interpreter from your conda environment.

   (addbio-processing) C:\Users\me> python

7. Test the OpenSim configuration by checking the timestamp from running 

   >>> import opensim
   >>> opensim.GetVersionAndDate()

   in the Python interpreter; the date should be in June 2023.

OpenSim with Matlab

1. Download the OpenSim 4.4.1 software.
   1. Windows: 
   2. Mac: 
2. Install OpenSim to a location of your choice; perhaps C:\opensim-core-4.4.1 (on Windows).
3. If you use Windows, edit your PATH environment variable to include OpenSim’s bin directory (e.g., C:\opensim-core-4.4.1\bin). See here for instructions. Make sure to remove any previous OpenSim installations from your PATH.

4. Configure OpenSim with Matlab by starting Matlab and running configureOpenSim.m, located at C:\opensim-core-4.4.1\Resources\Code\Matlab.

5. After the script completes, restart Matlab and test the configuration by checking the timestamp from running 

   >> org.opensim.modeling.opensimCommon.GetVersionAndDate()

    in the Matlab command window; the date should be in July 2023.

6. Test that the visualizer is working by running the following:

   >> cd 'C:\opensim-core-4.4.1\Resources\Code\Matlab\Moco'
   >> exampleSlidingMass

   You should see a visualizer window appear with a sliding mass animation. Hit ESC twice to close the window.

7. Note for OpenSim 3.3 users: You cannot concurrently use OpenSim 3.3 and 4.4 in Matlab; to switch versions you must re-run configureOpenSim.m and edit your PATH environment variable.

4. Download the example data

Download the example data: ExampleData.zip

Documentation

OpenSim API docs

View the OpenSim API documentation here.

Moco cheat sheet

Moco website

Visit the Moco website here.

Slides

TODO

Publications

Uhlrich SD, Falisse A, Kidzinski L, Muccini J, Ko M, Chaudhari AS, Hicks JL, Delp SL (2022) OpenCap: 3D human movement dynamics from smartphone videos. bioRxiv. https://doi.org/10.1101/2022.07.07.499061

Werling K, Bianco NA, Raitor M, Stingel J, Hicks JL, Collins SH, Delp SL, Liu CK (2023) AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization. bioRxiv. https://doi.org/10.1101/2023.06.15.545116

Dembia CL, Bianco NA, Falisse A, Hicks JL, Delp SL (2020) OpenSim Moco: Musculoskeletal optimal control. PLoS Comput Biol 16(12): e1008493. https://doi.org/10.1371/journal.pcbi.1008493

Workshop survey

Please fill out our post-workshop survey.  Thank you!