| A noninvasive technique is developed that may be used in the determination of joint pathology. The research is separated into two sections. In the first, the influence of the number of data points, data dispersion, and the magnitude of the measurement error on the calculation of the algebraic parameters of motion (proper orthogonal matrix and translation vector) from measured position data is studied. In the second part of the study, multi-planar, rigid body motion of the human knee joint is investigated.; Specifically, motion steps at the knee were studied via computer simulations to determine if the finite helical axis parameters could be utilized in a descriptive sense to define these motions. Overall results show that the singular value decomposition (SVD) is quite robust in determining the algebraic parameters of motion whereas the finite helical axis is quite noisy and is neither consistent nor reproducible. However, it is also shown that the rotation angle associated with the screw axis and the magnitude of the position vector locating the screw axis in space may be used in a qualitative sense to infer joint abnormalities or pathologies. Although the knee joint was used as a specific example in this study, the techniques developed are general enough that they may be applied to any set of articulating segments. |
