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The tutorial below is designed for use with OpenSim version 4.0 and later. A version of the tutorial compatible with OpenSim version 3.3 is available here. |
I. Objectives
Purpose
The purpose of this tutorial is to demonstrate how OpenSim solves an inverse kinematics and dynamics problem using experimental data. To diagnose movement disorders and study human movement, biomechanists frequently ask human subjects to perform movements in a motion capture laboratory and use computational tools to analyze these movements. A common step in analyzing a movement is to compute the joint angles and joint moments of the subject during movement. OpenSim has tools for computing these quantities:
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3. Click on the Scale Factors tab. Which body segments were scaled manually?
4. In the Scale Factors Tab, click on the Edit Measurements Set. Which marker pairs are used to determine the Right Femur scaling? Is this a good assumption? When is this not a good assumption?
5. Click on the Static Pose Weights tab. Which markers are used to determine the pose of the model?
IV. Inverse Kinematics
Kinematics is the study of motion without considering the forces and moments that produce that motion. The purpose of inverse kinematics (IK) is to estimate the joint angles of a particular subject from experimental data. In this section, you will estimate a subject's joint angles during walking by performing an IK analysis using the subject scaled model and experimentally collected walking data.
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To set up an inverse kinematics analysis:
subject01_Setup_IK.xml contains pre-configured settings for the inverse kinematics tool. Notice the text boxes in the dialog window are now filled with values. A detailed explanation of the Inverse Kinematics Tool can be found on the Inverse Kinematics page of the documentation.
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Navigate to the Weights tab.
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To perform inverse kinematics:
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Questions
64. In the inverse kinematics Tool Inverse Kinematics Tool dialog window, click the Weights tab and scroll through the list of markers in the top half of the weights tab. Which markers have weighting values less than one? Why?
Hint: Think about joints that have not been modeled.
75. Based on information in the Messages window, what is the root-mean-squared (RMS) error of all the markers in the last frame of the motion? Include units. Does this seem reasonable? Explain.
86. What was the value of the maximum marker error in the last frame? Include units. Which marker had this maximum error, and why?
Hint: Think about the weighted least squares problem.
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To setup an inverse dynamics analysis:
A detailed explanation of the Inverse Dynamics Tool can be found on the Inverse Dynamics page of the documentation. | |
To perform inverse dynamics:
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When completed, examine the results of the inverse dynamics solution by plotting the net moments at the left and right ankles:
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In solving the inverse dynamics problem, both kinematic data and force plate data were used, making this an over-determined problem. In other words, the problem has more equations than unknowns (i.e., degrees of freedom). Due to errors in the experimental motion data and inaccuracies in the musculoskeletal model, it turns out that Newton's second law is violated, or [3]. One method to handle this inconsistency is to compute and apply residual forces and moments to a particular body segment in the model, such that Newton's second law becomes: † An analogous equation relates the ground reaction moment, to the residual moment, . In this musculoskeletal model, the residuals are applied to the pelvis segment. To see the residuals from the inverse dynamics solution, in a new plot window, Plot pelvis_tx_force, pelvis_ty_force, and pelvis_tz_force versus time. Using this plot, answer question 9. While applying residual forces and moments makes the model's motion dynamically consistent with the external forces , this strategy is undesirable because the residuals can be large. More advanced strategies have been developed to deal with the problem of residuals and dynamic inconsistencies, such as least-squares optimization [3], the Residual Elimination Algorithm (REA) [5], and the Residual Reduction Algorithm (RRA) [6]. OpenSim implements a Residual Reduction Algorithm as part of its workflow for generating muscle-actuated simulations [6]. A detailed explanation of the Residual Reduction Algorithm (RRA) can be found on the Residual Reduction Algorithm page of the documentation. For additional information on these strategies, please also see [3], [5], [6], and [7]. |
Questions
97. On your plot of the ankle moments, identify when heel strike, stance phase, toe off, and swing phase occur for each curve (i.e., left leg and right leg).
108. Based on your plot and the angle convention for the ankle, give an explanation of what is happening at the ankle just before toe-off.
Hint: It may be useful to use the Coordinate sliders to understand the angle convention for the ankle.
119. What are the maximum magnitudes of the residual forces? Using the mass of the subject from Question 1, what fraction of body weight are the maximum residual forces?
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