exampleEMGTracking.py

This is an example that uses the MocoInverse tool and EMG data to create an EMG-driven simulation of walking. This example is used in hands-on workshops, and accordingly has blanks that users must fill in. See exampleSquatToStand_answers.m for a completed version.

 
import opensim as osim
import exampleEMGTracking_helpers as helpers
import os
import numpy as np
 
 
 
# Part 1a: Load a 19 degree-of-freedom model with 18 lower-limb,
# sagittal-plane muscles and a torque-actuated torso. This includes a set of
# ground reaction forces applied to the model via ExternalLoads, which is
# necessary for the muscle redundancy problem. See the function definition
# at the bottom of this file to see how the model is loaded and constructed.
model = helpers.getWalkingModel()
 
# Part 1b: Create the MocoInverse tool and set the Model.
 
 
# Part 1c: Create a TableProcessor using the coordinates file from inverse
# kinematics.
 
 
# Part 1d: Set the kinematics reference for MocoInverse using the
# TableProcessor we just created.
 
 
# Part 1e: Provide the solver settings: initial and final time, the mesh
# interval, and the constraint and convergence tolerances.
inverse.set_initial_time( )
inverse.set_final_time( )
inverse.set_mesh_interval( )
inverse.set_constraint_tolerance( )
inverse.set_convergence_tolerance( )
 
if not os.path.isfile('effortSolution.sto'):
    # Part 1f: Solve the problem!
 
 
 
emgReference = osim.TimeSeriesTable('emg.sto')
helpers.compareSolutionToEMG(emgReference, 'effortSolution.sto')
 
 
 
# Part 3a: Call initialize() to get access to the MocoStudy contained within
# the MocoInverse instance. This will allow us to make additional
# modifications to the problem not provided by MocoInverse.
 
 
# Part 3b: Create a MocoControlTrackingGoal, set its weight, and provide
# the EMG data as the tracking reference. We also need to specify the
# reference labels for the four muscles whose EMG we will track.
tracking = 
tracking.setWeight( )
tracking.setReference( )
tracking.setReferenceLabel('/forceset/gasmed_l', 'gastrocnemius')
tracking.setReferenceLabel('/forceset/tibant_l', 'tibialis_anterior')
tracking.setReferenceLabel('/forceset/bfsh_l', 'biceps_femoris')
tracking.setReferenceLabel( )
 
# Part 3c: The EMG signals in the tracking are all normalized to have
# a maximum value of 1, but the magnitudes of the excitations from the
# effort minimization solution suggest that these signals should be
# rescaled. Use addScaleFactor() to add a MocoParameter to the problem that
# will scale the reference data for the muscles in the tracking cost.
tracking.addScaleFactor('gastroc_factor', '/forceset/gasmed_l', [0.01, 1.0])
tracking.addScaleFactor('tibant_factor', '/forceset/tibant_l', [0.01, 1.0])
tracking.addScaleFactor('bifem_factor', '/forceset/bfsh_l', [0.01, 1.0])
tracking.addScaleFactor( )
 
# Part 3d: Add the tracking goal to the problem.
 
 
# Part 3e: Update the MocoCasADiSolver with the updated MocoProblem using
# resetProblem().
solver = osim.MocoCasADiSolver.safeDownCast(study.updSolver())
solver.resetProblem(problem)
 
# Part 3f: Tell MocoCasADiSolver that the MocoParameters we added to the
# problem via addScaleFactor() above do not require initSystem() calls on
# the model. This provides a large speed-up.
 
 
if not os.path.isfile('trackingSolution.sto'):
    # Part 3g: Solve the problem!
 
 
 
# Part 3h: Get the values of the optimized scale factors.
trackingSolution = osim.MocoTrajectory('trackingSolution.sto')
gastroc_factor = trackingSolution.getParameter('gastroc_factor')
tibant_factor = trackingSolution.getParameter('tibant_factor')
bifem_factor = trackingSolution.getParameter('bifem_factor')
gluteus_factor = 
 
 
print('\nOptimized scale factor values:')
print('------------------------------')
print('gastrocnemius = ' + str(gastroc_factor))
print('tibialis anterior = ' + str(tibant_factor))
print('biceps femoris short head = ' + str(bifem_factor))
print('gluteus = ' + str(gluteus_factor))
 
# Part 4b: Re-scale the reference data using the optimized scale factors.
gastroc = emgReference.updDependentColumn('gastrocnemius')
tibant = emgReference.updDependentColumn('tibialis_anterior')
bifem = emgReference.updDependentColumn('biceps_femoris')
gluteus = emgReference.updDependentColumn('gluteus')
for t in np.arange(emgReference.getNumRows()):
    t = int(t) # Convert to Python built-in int type for indexing
    gastroc[t] = gastroc_factor * gastroc[t]
    tibant[t] = tibant_factor * tibant[t]
    bifem[t] = bifem_factor * bifem[t]
    gluteus[t] = gluteus_factor * gluteus[t]
 
# Part 4c: Generate the plots. Compare results to the effort minimization
# solution.
helpers.compareSolutionToEMG(emgReference, 'effortSolution.sto',
    'trackingSolution.sto')