Open AccessVerification of Manual Digitization Methods During Experimental Simulation of Knee Motion
Author(s) -
Zachary Hargett,
Manuel Gutierrez,
Melinda K. Harman
Publication year - 2020
Publication title -
journal of verification, validation, and uncertainty quantification
Language(s) - English
Resource type - Journals
eISSN - 2377-2166
pISSN - 2377-2158
DOI - 10.1115/1.4048748
Subject(s) - stylus , digitization , computer science , standard deviation , fiducial marker , point cloud , coordinate system , kinematics , orientation (vector space) , point (geometry) , computer vision , artificial intelligence , mathematics , geometry , physics , statistics , classical mechanics
Cadaveric testing is a common approach for verifying mathematical models used in computational modeling work. In the case of a knee joint model for calculating ligament tension during total knee replacement (TKR) motion, model inputs include rigid body motions defined using the Grood-Suntay coordinate system as a spatial linkage between the tibial component orientation relative to the femoral component. Using this approach requires the definition of coordinate systems for each rigid TKR component (i.e. tibial and femoral) based on fiducial points, manual digitization of a point cloud within the experimental setup, and registration of the orientation relative to the relevant bone marker array. The purpose of this study was to compare the variability between two different manual point digitization methods (a hand-held stylus and pivot tool each calibrated in the optical tracking system), using a TKR femoral component in a simulated cadaver limb experimental setup as an example. This was accomplished by verifying the mathematical algorithm used to calculate the coordinate system from the digitized points, quantifying the variability of the manual digitization methods, and discussing how any error could affect the computational model. For the hand-held stylus method, the standard deviation of the origin and, x-, y-, and z-axis calculations were 0.50mm, 1.31 degrees, 0.51 degrees, and 0.62 degrees, respectively. It is important to note that there is an additional error created using the hand-held stylus from required manual digitization of each rigid marker array. This average additional error was 0.54mm for the origin and 1.70, 1.66, and 0.98 degrees for the x-, y-, and z-axes, respectively. For the pivot tool method, the standard deviation was 0.35mm, 0.37 degrees, 1.27 degrees, and 1.24 degrees for the origin, x-, y-, and z-axes, respectively. It is essential to minimize experimental error, as small errors in alignment can substantially alter model outputs. In this study of cadaver simulation of limb motion, the pivot tool is the better option for minimizing error. Careful definition of fiducial points and repeatable manual digitization of the point cloud is critical for meaningful computational models of TKR motion based on cadaver experimental work.