An Efficient Probabilistic Methodology for Incorporating Uncertainty in Body Segment Parameters and Anatomical Landmarks in Joint Loadings Estimated From Inverse Dynamics

Abstract

Inverse dynamics is a standard approach for estimating joint loadings in the lower extremity from kinematic and ground reaction data for use in clinical and research gait studies. Variability in estimating body segment parameters and uncertainty in defining anatomical landmarks have the potential to impact predicted joint loading. This study demonstrates the application of efficient probabilistic methods to quantify the effect of uncertainty in these parameters and landmarks on joint loading in an inverse-dynamics model, and identifies the relative importance of the parameters and landmarks to the predicted joint loading. The inverse-dynamics analysis used a benchmark data set of lower-extremity kinematics and ground reaction data during the stance phase of gait to predict the three-dimensional intersegmental forces and moments. The probabilistic analysis predicted the 1-99 percentile ranges of intersegmental forces and moments at the hip, knee, and ankle. Variabilities, in forces and moments of up to 56% and 156% of the mean values were predicted based on coefficients of variation less than 0.20 for the body segment parameters and standard deviations of 2 mm for the anatomical landmarks. Sensitivity factors identified the important parameters for the specific joint and component directions. Anatomical landmarks affected moments to a larger extent than body segment parameters. Additionally, for forces, anatomical landmarks had a larger effect than body segment parameters, with the exception of segment masses, which were important to the proximal-distal joint forces. The probabilistic modeling approach predicted the range of possible joint loading, which has implications in gait studies, clinical assessments, and implant design evaluations.

Whole body inverse dynamics over a complete gait cycle based only on measured kinematics

“…For example, the overestimated braking force in early stance phase may be due to soft tissue motion immediately after heel strike. Although skin movements are relatively small in comparison to sagittal plane motions, they can be significant in comparison to the motions in the other planes (Reinschmidt et al, 1997;Manal et al, 2000;Leardini et al, 2005). This may explain the poor accuracy of the non-sagittal plane ground reactions.…”

“…A likely cause of the errors in the estimated ground reactions is skin movement artefact, which has been shown to be a potential source of significant errors (Reinschmidt et al, 1997;Manal et al, 2000;Leardini et al, 2005;Garling et al, 2007;Nester et al, 2007). For example, the overestimated braking force in early stance phase may be due to soft tissue motion immediately after heel strike.…”

“…Previous gait models based only on measured kinematics (Thornton-trump and Daher, 1975;Hardt and Mann, 1979;Bobbert et al, 1991;Koopman et al, 1995) have been for running gaits only (no double support phase) or relatively simple, representing the upper body as only one segment, and were not validated against simultaneously collected force plate data. Validation and error analysis are essential as uncertainties or errors are very likely in the data used in inverse dynamics calculations, e.g., inertial property data (Pearsall and Costigan, 1999;Rao et al, 2006;Silva and Ambró sio, 2004), joint parameters (Holden and Stanhope, 1998;Stagni et al, 2000), anatomical landmarks (Langenderfer et al, 2008) and noisy kinematic data (Winter, 1990;Bisseling and Hof, 2006). This paper presents a three-dimensional (3D) whole body multi-segment model for inverse dynamics analysis over a complete gait cycle, based only on measured kinematic data.…”

The influence of cycle time on shoulder fatigue responses for a fixed total overhead workload

“…A probabilistic analysis of altering the origin and insertion locations of upper and lower extremity muscles calculated differences in normalized predicted force magnitudes up to 51% (Chopp-Hurley et al, 2014) and moment arm length differences up to 41.3 mm (Pal et al, 2007), between 1% and 99% confidence intervals, respectively. Similarly, differences in body segment parameters and anatomical landmark uncertainty has resulted in a spectrum of force and moment predictions, with differences between 1% and 99% confidence limits as high as 53.6 N and 8.9 Nm, respectively for lower-extremity intersegmental forces and moments (Langenderfer et al, 2008). Experimentally, soft tissue and bone shape measurements vary considerably across a population.…”

A review of probabilistic analysis in orthopaedic biomechanics

“…3 Probabilistic application in a hip construct from Dopico-Gonzalez et al [28]. Three bones were implanted with two uncemented designs: (a) variable parameters including implant orientation, muscle forces, and loading, (b) a sensitivity analysis showing the effect of variable parameters on micromotion for each bone and design probabilistic techniques (AMV) and also presented sensitivity factors to identify the most important input parameters [45]. The sensitivity of joint mechanics predictions has been investigated with computational models by perturbing individual parameters, including defined axes, component alignment, and material representations for cartilage, ligaments, muscles, and intervertebral discs.…”