Healthy Gait

• Open the scenario Tutorials/Tutorial 4a - Gait

As you can see, the scone file is fairly short and doesn't contain a Controller or a Measure. However, there is a section in the middle that says:

# Controller for gait, based on [Geyer & Herr 2010]
<< data/ControllerGH2010.scone >>
		
# Measure for gait
<< data/MeasureGait10.scone >>

What this means is, we have moved the controller and measure code to different files, in order to keep things tidy. Before we look into any details, start the optimization (this will take a while).

• Click Scenario → Optimize Scenario or press Ctrl + F5

The actual Reflex Gait Controller is defined inside the file Tutorials/data/ControllerGH2010.scone, which implements the gait controller defined by [Geyer & Herr 2010]. The key components used in this Controller are the GaitStateController, which is used to detect different gait phases (stance, lift-off, swing, etc.), and a number of ReflexController that are active during one or more phases. Please see Tutorials/data/ControllerGH2010.scone for more details.

The actual parameters that are being optimized are again shown in the Parameters Window (Windows → Parameters). These include the reflex gains and offsets of the gait controller, as well as parameters that set the initial pose.

In this example, not only the control parameters are being optimized, but also the initial pose of the model. This is done via:

# Optimize initial state parameters
state_init_file = data/InitStateGait10.zml
initial_state_offset = 0~0.01<-0.5,0.5>
initial_state_offset_exclude = "*_tx;*_ty;*_u"

Here, the state_init_file defines the initial pose, and initial_state_offset defines optimization parameters for offsets that are added to this pose. In this example, translation states and coordinate velocities are excluded from the optimization, as indicated by initial_state_offset_exclude

• Open the file Tutorials/Tutorial 4b - Fast Gait.scone

To optimize gait for different target speeds, we can simply change the optimization criterion. In this case, we define the optimization criterion in the file Tutorials/data/MeasureGait15.scone:

# Measure for gait, minimum speed = 1.5 m/s
CompositeMeasure {
	GaitMeasure {
		name = Gait
		weight = 100
		threshold = 0.05
		termination_height = 0.85
		min_velocity = 1.5
	}
	EffortMeasure {
		name = Effort
		weight = 0.1
		measure_type = Wang2012
		use_cost_of_transport = 1
	}
	CompositeMeasure {
		name = DofLimits
		symmetric = 1
		DofMeasure {
			weight = 0.1
			dof = ankle_angle
			position { min = -60 max = 60 squared_penalty = 1 }
		}
		DofMeasure {
			weight = 0.01
			threshold = 5
			dof = knee_angle
			force { min = 0 max = 0 abs_penalty = 1 }
		}
	}
}

The target speed is set by the min_velocity tag in the GaitMeasure section. In addition, and EffortMeasure is used to minimize cost of transport, and DofMeasure is used to prevent knee and ankle hyper-extension.

• Open the file Tutorials/Tutorial 4c - Perturbed Gait.scone

Here you will see that we use a CompositeController that consists of our Geyer and Herr controller, in combination with a couple of PerturbationController entries:

CompositeController {
	# Controller for gait, based on [Geyer & Herr 2010]
	<< data/ControllerGH2010.scone >>
 
	# Perturbation backwards every 4 seconds
	PerturbationController {
		start_time = 3
		duration = 0.2
		interval = 4
		force = [ -100 0 0 ]
		body = pelvis
		position_offset = [ -0.15 0.35 0 ]
	}
 
	# Perturbation forwards every other 4 seconds
	PerturbationController {
		start_time = 5
		duration = 0.2
		interval = 4
		force = [ 100 0 0 ]
		body = pelvis
		position_offset = [ -0.15 0.35 0 ]
	}
}

Note that inside SCONE, a PerturbationController is a controller just like anything else, even though it does not work on internal actuators. A SCONE Controller is simply any entity that can generate actuator inputs in the simulation.

• Test the optimization by pressing Ctrl + T

You'll see that, once the first perturbation kicks in, the model falls over immediately. The control parameters of the model have not yet been optimized to be robust against external perturbations.

• Start the optimization with Ctrl + F5

After a while, you'll find that the gait controller will become more robust against perturbations. Note that we have increased the simulation time 30 seconds: can you think of a reason why?

To simulate walking on a slippery slope, we need to adjust the friction defined in the contact force. For this tutorial, we have created a separate model in which we have done exactly that (see Tutorials/data/H0914MSS_osim3.osim).

The items that have changed are called static_friction, dynamic_friction and viscous_friction.

• Open the file Tutorials/Tutorial 4d - Slippery Slope

In our SCONE scenario, we simply use the new model file:

model_file = data/H0914MSS_osim3.osim

We also change the objective function to allow for a lower minimum velocity:

<< data/MeasureGait05.scone >>

• Optimize the scenario with the modified OpenSim model (Ctrl + F5)

Eventually, this will produce a controller that is robust against walking on a slippery slope. Be sure to check the intermediate results in the Optimization Results panel!