Notebook Entry

Today's task list:

  • [x] Work on parsing the walking data
  • [] Work on BMD papers
  • [] Book hotel for BMD
  • [] Post update about BMD copyright
  • [] Finish reading the van der Kooij paper
  • [] See if our controller can drive an OpenSim model or Ton's 2D model
  • [] Wrap the HBM C code
  • [] Duplicate website backups on a S3 bucket
  • [~] Work on the website theme
  • [] Make generic settings on the lab website
  • [] Review the TODO items on the Yeadon paper
  • [] Do CITI course
  • [] Do FERPA course
  • [] Write up database proposal
  • [] Try out CSympy with some mechanics problems
  • [] Email Mounir about teaching

Walking System Identification

The walking control identification is is pretty good shape now. At this point we need to try it on more data that includes perturbations. I've written a couple of classes with pretty good unit test coverage that handle managing walking data and identifying the gains in a simple control scheme for walking which can be found here:

The maths and problem framework that guided the above code are explained here:

I use it to analyze a couple of data sets, one from Ton and one from Obinna, of normal unperturbed walking. The basic results are presented here:

I believe the method works computationally and is giving correct results for the given data. I haven't spent much time thinking about the usefulness of the current results yet. The two walkers give very different looking gains.

So now we are ready to try it on some perturbed data and I'm also planning to see if the identified controller(s) can make a simple walking model walk. I'll soon add how well the controller can predict data that wasn't used for the identification.

I'm soliciting comments on what kinds of graphs are useful, how to present the data, if this is a reasonable method, or anything else, please let em rip.