# Part A: KinematicsTask 1: Calculate functional hip joint centre using optimisati

Part A: KinematicsTask 1: Calculate functional hip joint centre using optimisation method. Use the optimal hip centre location algorithm of Gamage and Lasenby (2002) (use Jupyter notebook from Tutorial 1) to calculate a functional hip joint centre for the left and right hip joint using the C3D trials provided (jw_hip_lstar1.c3d and jw_hip_rstar1.c3d). You will need to export the marker trajectories as a .trc file using Mokka (double check to make sure the marker labels match what is in your python script…they are case sensitive!). Filter your marker data using a low-pass Butterworth filter with a cut-off frequency that you think is reasonable. Determine the x, y, z locations of the left and right hip joint centre in the pelvis coordinate system (refer to the ‘ISB standard hip CS.pdf’ document). Task 2: Calculate hip joint centre using regression equations. Tylkowski et al. (1982) published a paper with an updated set of parameters of a linear regression to estimate the hip joint centre, based upon the distance (d) between the left and right superior iliac spines (LASIS and RASIS, respectively). The parameters describe the position of the hip joint centre relative to the ASIS marker, as shown in Figure 1 below. Use the static standing trial (jw_staticfor.c3d) to obtain the anatomical landmarks. Determine the x, y, z locations of the left and right hip joint centre in the pelvis coordinate system. Reference: Tylkowski, C. M., Simon, S. R., & Mansour, J. M. (1982). The Frank Stinchfield Award Paper. Internal rotation gait in spastic cerebral palsy. The Hip, 89–125. Figure 1. Tylkowski et al. (1982) regression parameters to predict the hip joint centre of an adult pelvis, given the distance, d, between the anterior superior iliac spine landmarks. The blue points indicate the predicted hip joint centre and red points are the actual hip centre from a segmented CT scan. Task 3: Scale the generic OpenSim gait model to match the experimental markers. Open the gait2354_simbody.osim model file and attach the markers from the Grand Knee Challenge marker set (GrandKneeMarkerSet.xml) (ignore the upper limb markers). Use the experimental markers from the static trial (jw_staticfor.c3d) to scale the pelvis, thigh, shank, and foot segments of the OpenSim model. Save the scaled .osim file and then search in the model file for the x, y, z location of the left and right hip joint centres. Record these values with respect to the pelvis coordinate system. Task 4: Add two more dofs to the knee model. The default knee in this model has only one degree of freedom (flexion-extension). If we want to calculate the net joint moment about other axes (abduction-adduction or internal-external rotation) we will have to include these DOFs in the model. Modify the scaled model from Task 3 using Notepad++ to include knee internal-external rotation and adduction-abduction (as shown during Tutorial 4). Place appropriate constraints on these new DOFs. Task 5: Perform inverse kinematics (IK). Perform IK on the normal over ground walking trial (jw_ngait_og1.c3d) using the scaled model from Task 3. You will need to ensure that the y-axis is vertical, so after you export the .trc file from Mokka, use the python script provided from Tutorial 3 to rotate the marker data. You can use equal weighting for all markers when you generate the IK solution. Save the resulting .mot file, as you will need to generate some graphs from these generalized coordinates. ax = 0.22 ay
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