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visual3d:documentation:modeling:functional_joints:functional_joints [2025/01/24 18:26] – wikisysopvisual3d:documentation:modeling:functional_joints:functional_joints [2025/09/05 15:04] (current) – sgranger
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 adapted from: adapted from:
-[[[http://www.sciencedirect.com/science/article/pii/S002192900400137X|Schwartz MH, Rozumalski A (2005) A new method for estimating joint parameters from motion data. Journal of Biomechanics, 38, 107-116]]]+[[http://www.sciencedirect.com/science/article/pii/S002192900400137X|Schwartz MH, Rozumalski A (2005) A new method for estimating joint parameters from motion data. Journal of Biomechanics, 38, 107-116]]
 Specify a segment coordinate system in which the motion capture data is to be resolved, and into which the landmark represented. For the case of the hip joint center, for example, the pelvis is considered a stationary coordinate system. Specify the motion capture markers attached to the moving segment. For the case of the hip joint center, markers attached to the thigh are used. Specify a segment coordinate system in which the motion capture data is to be resolved, and into which the landmark represented. For the case of the hip joint center, for example, the pelvis is considered a stationary coordinate system. Specify the motion capture markers attached to the moving segment. For the case of the hip joint center, markers attached to the thigh are used.
  
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 adapted from adapted from
-[[http://www.sciencedirect.com/science/article/pii/S0021929016303979|Jensen E, Lugade V, Crenshaw J, Miller E, Kaufman K (2016) A principal component analysis approach to correcting the knee flexion axis during gait. Journal of Biomechanics, in press]]+[[http://www.sciencedirect.com/science/article/pii/S0021929016303979|Jensen E, Lugade V, Crenshaw J, Miller E, Kaufman K (2016) A principal component analysis approach to correcting the knee flexion axis during gait]]
 Abstract Abstract
  
 Accurate and precise knee flexion axis identification is critical for prescribing and assessing tibial and femoral derotation osteotomies, but is highly prone to marker misplacement-induced error. The purpose of this study was to develop an efficient algorithm for post-hoc correction of the knee flexion axis and test its efficacy relative to other established algorithms. Gait data were collected on twelve healthy subjects using standard marker placement as well as intentionally misplaced lateral knee markers. The efficacy of the algorithm was assessed by quantifying the reduction in knee angle errors. Crosstalk error was quantified from the coefficient of determination (r2) between knee flexion and adduction angles. Mean rotation offset error (αo) was quantified from the knee and hip rotation kinematics across the gait cycle. The principal component analysis (PCA)-based algorithm significantly reduced r2 (p<0.001) and caused αo,knee to converge toward 11.9±8.0° of external rotation, demonstrating improved certainty of the knee kinematics. The within-subject standard deviation of αo,hip between marker placements was reduced from 13.5±1.5° to 0.7±0.2° (p<0.001), demonstrating improved precision of the knee kinematics. The PCA-based algorithm performed at levels comparable to a knee abduction–adduction minimization algorithm ( Baker et al., 1999 ) and better than a null space algorithm ( Schwartz and Rozumalski, 2005 ) for this healthy subject population. Accurate and precise knee flexion axis identification is critical for prescribing and assessing tibial and femoral derotation osteotomies, but is highly prone to marker misplacement-induced error. The purpose of this study was to develop an efficient algorithm for post-hoc correction of the knee flexion axis and test its efficacy relative to other established algorithms. Gait data were collected on twelve healthy subjects using standard marker placement as well as intentionally misplaced lateral knee markers. The efficacy of the algorithm was assessed by quantifying the reduction in knee angle errors. Crosstalk error was quantified from the coefficient of determination (r2) between knee flexion and adduction angles. Mean rotation offset error (αo) was quantified from the knee and hip rotation kinematics across the gait cycle. The principal component analysis (PCA)-based algorithm significantly reduced r2 (p<0.001) and caused αo,knee to converge toward 11.9±8.0° of external rotation, demonstrating improved certainty of the knee kinematics. The within-subject standard deviation of αo,hip between marker placements was reduced from 13.5±1.5° to 0.7±0.2° (p<0.001), demonstrating improved precision of the knee kinematics. The PCA-based algorithm performed at levels comparable to a knee abduction–adduction minimization algorithm ( Baker et al., 1999 ) and better than a null space algorithm ( Schwartz and Rozumalski, 2005 ) for this healthy subject population.
  
-==== Defining a Functional Joint ==== +==== Examples ==== 
- +[[visual3d:documentation:modeling:functional_joints:defining_a_functional_joint]] \\ 
-==== Functional Joints Post Processing ==== +[[visual3d:documentation:modeling:functional_joints:functional_joints_post_processing]] \\ 
- +[[visual3d:documentation:pipeline:model_commands:add_functional_joint_landmark]] \\ 
-==== Functional Joints from Streaming Data ==== +[[visual3d:documentation:modeling:functional_joints:example_-_functional_hip]] \\ 
- +[[visual3d:documentation:modeling:functional_joints:example_-_functional_knee]]
-==== Add_Functional_Joint_Landmark ==== +
- +
-==== Example: Functional Joint ==== +
- +
-=== Example: Functional Hip === +
- +
-=== Example: Functional Knee === +
- +
- +
visual3d/documentation/modeling/functional_joints/functional_joints.1737743170.txt.gz · Last modified: 2025/01/24 18:26 by wikisysop