Comparative assessment of knee joint models used in multi-body kinematics optimisation for soft tissue artefact compensation

Abstract

Estimating joint kinematics from skin-marker trajectories recorded using stereophotogrammetry is complicated by soft tissue artefact (STA), an inexorable source of error. One solution is to use a bone pose estimator based on multi-body kinematics optimisation (MKO) embedding joint constraints to compensate for STA. However, there is some debate over the effectiveness of this method. The present study aimed to quantitatively assess the degree of agreement between reference (i.e., artefact-free) knee joint kinematics and the same kinematics estimated using MKO embedding six different knee joint models. The following motor tasks were assessed: level walking, hopping, cutting, running, sit-to-stand, and step-up. Reference knee kinematics was taken from pin-marker or biplane fluoroscopic data acquired concurrently with skin-marker data, made available by the respective authors. For each motor task, Bland-Altman analysis revealed that the performance of MKO varied according to the joint model used, with a wide discrepancy in results across degrees of freedom (DoFs), models and motor tasks (with a bias between -10.2° and 13.2° and between -10.2mm and 7.2mm, and with a confidence interval up to ±14.8° and ±11.1mm, for rotation and displacement, respectively). It can be concluded that, while MKO might occasionally improve kinematics estimation, as implemented to date it does not represent a reliable solution to the STA issue.

Evaluation of a multibody kinematics optimization method for three-dimensional canine pelvic limb gait analysis

“…However, except for spherical and hinge type models, other existing joint model designs require precise geometry of bone, articulation, and ligaments [24,25,28]. In addition, a previous report suggested that the best joint model type may be motor task-dependent [27]. A thorough assessment of MKO embedding more complex joint models is warranted for a better description of pelvic limb kinematics during canine gait.…”

“…MKO was introduced to provide another STA-compensated solution for the estimation of 3-D kinematics of the pelvic limb by minimizing the differences between model-determined and measured skin marker coordinates using a global optimization strategy [22,23]. The MKO approach and its modifications have been widely assessed for their STA compensation capability in human gait analysis [23][24][25][26][27][28]. MKO offers the advantage that it can be implemented with conventional 3-D marker sets and therefore could provide a possible alternative for STA compensation in canine motion analysis.…”

A Novel Ultrasound-Based Lower Extremity Motion Tracking System

“…However, the fact is that human skeletal structures are not exposed to the outside environment but are surrounded by the soft tissues (the muscles, the fat, the skin, etc.). Therefore, an effective measuring technique that could directly or indirectly detect the motion of the bone is necessary to monitor and trace the movements of bone segments underlying the skin surface [12,13].…”

“…The propagation of STA to knee joint kinematics has been reported to lead to average rotational errors of up to 4.4 • and 13.1 • and average translational errors of up to 13.0 mm and 16.1 mm for walking and cutting motions, respectively [17]. Although many researchers attempted to compensate for the STA by computer modeling [13,[18][19][20][21][22][23][24], no significant improvement has been found in previous studies [12].…”

The Impact Of Thigh And Shank Marker Quantity On Lower Extremity Kinematics Using A Constrained Model

“…A recent special edition of the Journal of Biomechanics proposed new and innovative techniques to mitigate some of the effects of softtissue artifact [2]. While these techniques improve the overall fidelity of motion capture data, they introduce new challenges to both the collection and processing workflows [3][4][5][6][7]. This study takes a different approach to the problem; instead, seeking to understand how currently implemented techniques can be streamlined to preserve tolerable fidelity compared to unconstrained-kinematic models while reducing the burdens placed on subjects and researchers.…”