Joint center location is the driving parameter for determining the kinematics, and later kinetics, associated with human motion capture. Therefore the accuracy with which said location is determined is of great import to any and all subsequent calculation and analysis. The most significant barrier to accurate determination of this parameter is soft tissue artifact, which contaminates the measurements of on-body measurement devices by allowing them to move relative to the underlying rigid bone. This leads to inaccuracy in both bone pose estimation and joint center location. The complexity of soft tissue artifact (it is nonlinear, multimodal, subject-specific, and trial specific) makes it difficult to model, and therefore difficult to mitigate.
This thesis proposes a novel method, termed Single Frame Optimization, for determining joint center location (though mitigation of soft tissue artifact) via a linearization approach, in which the optimal vector relating a joint center to a corresponding inertial sensor is calculated at each time frame. This results in a time-varying joint center location vector that captures the relative motion due to soft tissue artifact, from which the relative motion could be isolated and removed. The method’s, and therefore the optimization’s, driving assumption is that the derivative terms in the kinematic equation are negligible relative to the rigid terms. More plainly, it is assumed that any relative motion can be assumed negligible in comparison with the rigid body motion in the chosen data frame. The validity of this assumption is investigated in a series of numerical simulations and experimental investigations. Each item in said series is presented as a chapter in this thesis, but retains the format of a standalone article. This is intended to foment critical analysis of the method at each stage in its development, rather than solely in its practical (and more developed) form.