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— | tutorials:pathological_gait [2022/01/21 11:11] (current) – thomas | ||
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+ | ====== Pathological Gait ====== | ||
+ | <callout type=" | ||
+ | This tutorial demonstrates how to investigate the effects of potential gait pathologies in a predictive simulation, based on the model by [Geyer & Herr 2010]. | ||
+ | </ | ||
+ | ==== Plantarflexor Weakness ==== | ||
+ | * Open the scenario '' | ||
+ | In this scenario, we override some of the model properties of the OpenSim model: | ||
+ | <code python> | ||
+ | # Override model properties | ||
+ | Properties { | ||
+ | gastroc_l { max_isometric_force.factor = 0.2 } | ||
+ | gastroc_r { max_isometric_force.factor = 0.2 } | ||
+ | soleus_l { max_isometric_force.factor = 0.2 } | ||
+ | soleus_r { max_isometric_force.factor = 0.2 } | ||
+ | } | ||
+ | </ | ||
+ | |||
+ | Here, we adjust the '' | ||
+ | |||
+ | * Optimize the scenario ('' | ||
+ | |||
+ | When playing back the intermediate results, you might notice that the muscle belly in the 3D viewer will be much thinner than in the original optimization. This is because the visualization is scaled to the maximum isometric force of the model. | ||
+ | |||
+ | ==== Short Hamstrings ==== | ||
+ | * Open the scenario '' | ||
+ | |||
+ | In this scenario, we override the contractile element length of the model' | ||
+ | <code python> | ||
+ | # Override model properties | ||
+ | Properties { | ||
+ | hamstrings_l { optimal_fiber_length.factor = 0.75 } | ||
+ | hamstrings_r { optimal_fiber_length.factor = 0.75 } | ||
+ | } | ||
+ | </ | ||
+ | |||
+ | * Optimize the scenario ('' | ||
+ | |||
+ | ==== Hyper-reflexia ==== | ||
+ | * Open the scenario '' | ||
+ | |||
+ | In this scenario we do not change any of the model parameters; instead, we add an additional [[ref: | ||
+ | |||
+ | <code python> | ||
+ | # Mimick hyper-reflexia through F+ reflex in gastroc | ||
+ | ReflexController { | ||
+ | symmetric = 1 | ||
+ | MuscleReflex { target = gastroc delay = 0.020 C0 = 0.1 KF = 1 } | ||
+ | } | ||
+ | </ | ||
+ | |||
+ | In this example, we add a constant excitation of 0.1 and an F+ reflex with a gain of 1. | ||
+ | |||
+ | * Optimize the scenario ('' | ||
+ | |||
+ | After a while, you will find that the controller adapts a toe-stepping gait. |