Bone orientation and position estimation errors using Cosserat point elements and least squares methods: Application to gait

Abstract

The aim of this study was to analyze the accuracy of bone pose estimation based on sub-clusters of three skinmarkers characterized by triangular Cosserat point elements (TCPEs) and to evaluate the capability of four instantaneous physical parameters, which can be measured non-invasively in-vivo, to identify the most accurate TCPEs. Moreover, TCPE pose estimations were compared with the estimations of two least squares minimization methods applied to the cluster of all markers, using rigid body (RBLS) and homogeneous deformation (HDLS) assumptions. Analysis was performed on previously collected in-vivo treadmill gait data composed of simultaneous measurements of the gold-standard bone pose by bi-plane fluoroscopy tracking the subjects' knee prosthesis and a stereophotogrammetric system tracking skin-markers affected by soft tissue artifact. Femur orientation and position errors estimated from skin-marker clusters were computed for 18 subjects using clusters of up to 35 markers. Results based on gold-standard data revealed that instantaneous subsets of TCPEs exist which estimate the femur pose with reasonable accuracy (median root mean square error during stance/swing: 1.4/2.8 deg for orientation, 1.5/4.2 mm for position). A non-invasive and instantaneous criteria to select accurate TCPEs for pose estimation (4.8/7.3 deg, 5.8/12.3 mm), was compared with RBLS (4.3/6.6 deg, 6.9/16.6 mm) and HDLS (4.6/7.6 deg, 6.7/12.5 mm). Accounting for homogeneous deformation, using HDLS or selected TCPEs, yielded more accurate position estimations than RBLS method, which, conversely, yielded more accurate orientation estimations. Further investigation is required to devise effective criteria for cluster selection that could represent a significant improvement in bone pose estimation accuracy.

A Framework for Measuring the Time-Varying Shape and Full-Field Deformation of Residual Limbs Using 3-D Digital Image Correlation

“…4(g) illustrates the value of the displacement magnitude calculated on each measurement point. Next, the deformation gradient tensor is calculated for each triangular element and for each configuration, with respect to the reference configuration [28], [29]. It should be noted that even though the reference configuration defines a zero-strain state in this analysis, it does not necessarily reflect a stress-free state of the skin.…”

Conclusion or Illusion: Quantifying Uncertainty in Inverse Analyses From Marker-Based Motion Capture due to Errors in Marker Registration and Model Scaling

“…There are three key limitations of this study. First, although marker registration and model scaling are not just issues when performing constrained inverse kinematics, we only tested kinematically constrained models—that is, we did not explicitly evaluate the effects of uncertainty using “unconstrained” modeling strategies ( Solav et al, 2017 ). We also did not explore computing kinematics with data from inertial measurement units (IMUs), but we expect that similar effects would be observed due to the uncertainty in the placement of IMUs on the body.…”

Synthetized inertial measurement units (IMUs) to evaluate the placement of wearable sensors on human body for motion recognition

“…This may explain differences on the thigh as skins and flesh lead to artefacts as well as sensor misplacement. Soft tissue can add up to 50% additive noise on acceleration and orientation measurements compared to actual bone motion [17]. Some joints are approximated by combinations of kinematic joints, as it is an osteo-articular model.…”