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

Abstract

Estimating kinematics from optical motion capture with skin-mounted markers, referred to as an inverse kinematic (IK) calculation, is the most common experimental technique in human motion analysis. Kinematics are often used to diagnose movement disorders and plan treatment strategies. In many such applications, small differences in joint angles can be clinically significant. Kinematics are also used to estimate joint powers, muscle forces, and other quantities of interest that cannot typically be measured directly. Thus, the accuracy and reproducibility of IK calculations are critical. In this work, we isolate and quantify the uncertainty in joint angles, moments, and powers due to two sources of error during IK analyses: errors in the placement of markers on the model (marker registration) and errors in the dimensions of the model’s body segments (model scaling). We demonstrate that IK solutions are best presented as a distribution of equally probable trajectories when these sources of modeling uncertainty are considered. Notably, a substantial amount of uncertainty exists in the computed kinematics and kinetics even if low marker tracking errors are achieved. For example, considering only 2 cm of marker registration uncertainty, peak ankle plantarflexion angle varied by 15.9°, peak ankle plantarflexion moment varied by 26.6 N⋅m, and peak ankle power at push off varied by 75.9 W during healthy gait. This uncertainty can directly impact the classification of patient movements and the evaluation of training or device effectiveness, such as calculations of push-off power. We provide scripts in OpenSim so that others can reproduce our results and quantify the effect of modeling uncertainty in their own studies.

Capacitive Sensing for Natural Environment Biomechanics Monitoring

“…Accuracy did not differ significantly with respect to the other degrees of freedom (all p-values ≥ 0.1107). On the other hand, IMU+CS simulations improved kinematic estimates with respect to IMU-only simulations for hip flexion by 8.0° ± 3.7° (p < 0.0001), hip adduction by 4.7° ± 2.2° (p < 0.0001), hip rotation by 11.3° ± 4.6° (p < 0.0001), knee flexion by 8.2° ± 3.4° (p = 0.0011), and ankle dorsiflexion by 6.9° ± 3.4° (p < 0.0001), achieving remarkable agreement with marker-based motion capture at each degree of freedom [41] . The IMU+CS approach was stable over multiple gait cycles.…”

Does enforcing glenohumeral joint stability matter? A new rapid muscle redundancy solver highlights the importance of non-superficial shoulder muscles

“…Our analysis addresses the issue that for any given marker error, there are infinite solutions for joint angles that can be within that error and inverse kinematics simply provides one solution, which is not the mean or the most representative of all possible solutions. Consequently, when employing inverse kinematic to estimate the joint angle trajectories corresponding to the marker data, the uncertainty in the placement of the markers or in the scaling of the model can have a significant impact on the conclusions of a biomechanical study [ 43 ]. We focus on the uncertainty from marker placement on the model, as the uncertainty of model scaling affected derived joint angles, moments and powers in a very similar way [ 43 ].…”

“…Consequently, when employing inverse kinematic to estimate the joint angle trajectories corresponding to the marker data, the uncertainty in the placement of the markers or in the scaling of the model can have a significant impact on the conclusions of a biomechanical study [ 43 ]. We focus on the uncertainty from marker placement on the model, as the uncertainty of model scaling affected derived joint angles, moments and powers in a very similar way [ 43 ].…”

A Method to Track 3D Knee Kinematics by Multi-Channel 3D-Tracked A-Mode Ultrasound

“…However, its invasive nature impedes its clinical application. As an alternative, skin-mounted markers are widely used to measure the kinematics of the lower extremity for gait analysis, where the trajectories of skin markers represent the movements of the underlying bone segments [ 3 , 4 , 5 ]. This method, however, is constrained by its lower accuracy due to soft tissue artifacts (STAs) [ 6 ].…”