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Efforts to compare different surface marker configurations in 3-dimensional motion analysis are warranted as more complex and custom marker sets become more common. At the knee, different markers can been used to represent the proximal shank. Often, two anatomical markers are placed over the femoral condyles, with their midpoint defining both the distal thigh and proximal shank segment ends. However, two additional markers placed over the tibial plateaus have been used to define the proximal shank end. For this experiment, simultaneous data for both proximal shank configurations were independently collected at two separate laboratories by different investigators, with one laboratory capturing a walking population and the other a running population. Common discrete knee joint variables were then compared between marker sets in each population. Using the augmented marker set, peak knee flexion after weight acceptance was less (1.2−1.7°, P < .02) and peak knee adduction was greater (0.7−1.4°, P < .001) in both data sets. Similarly, the calculated peak knee flexion moment was less by 15–20% and internal rotation moment was greater by 11–18% (P < .001). These results suggest that the calculation of knee joint mechanics are influenced by the proximal shank’s segment endpoint definition, independent of dynamic task, investigator, laboratory environment, and population in this study.

Knee valgus angles measured in sidestep cutting and vertical drop jumps are key variables in research on anterior cruciate ligament (ACL) injury causation. These variables are also used to quantify knee neuromuscular control and ACL injury risk. The aims of the current study were to (1) quantify the differences in the calculated knee valgus angles between 6 different thigh marker clusters, (2) investigate the trial ranking based on their knee valgus angles, and (3) investigate the influence of marker clusters on the cross-talk effect. Elite female handball and football players (n = 41) performed sidestep cutting and vertical drop jumping motions. We found systematic differences up to almost 15° of peak valgus between the marker sets in the drop jump test. The Spearman’s rank correlation coefficient varied from .505 to .974 among the 6 marker sets. In addition, the cross-talk effect varied considerably between the marker clusters. The results of the current study indicate that the choice of thigh marker cluster can have a substantial impact on the magnitude of knee valgus angle, as well as the trial ranking. A standardized thigh marker cluster, including nonanatomical landmark, is needed to minimize the variation of the measurement.

The purpose of this study was to quantify the effect of soft tissue artifact during three-dimensional motion capture and assess the effectiveness of an optimization method to reduce this effect. Four subjects were captured performing upper-arm internal-external rotation with retro-reflective marker sets attached to their upper extremities. A mechanical arm, with the same marker set attached, replicated the tasks human subjects performed. Artificial sinusoidal noise was then added to the recorded mechanical arm data to simulate soft tissue artifact. All data were processed by an optimization model. The result from both human and mechanical arm kinematic data demonstrates that soft tissue artifact can be reduced by an optimization model, although this error cannot be successfully eliminated. The soft tissue artifact from human subjects and the simulated soft tissue artifact from artificial sinusoidal noise were demonstrated to be considerably different. It was therefore concluded that the kinematic noise caused by skin movement artifact during upper-arm internal-external rotation does not follow a sinusoidal pattern and cannot be effectively eliminated by an optimization model.

This research tested a reproducible uneven walkway designed to destabilize human gait. Ten participants walked 30 times over even and uneven (7.3 × .08 m, sequentially-placed wooden blocks in a rotating pattern, 1-cm thick rubber mat) walkways. A full-body marker set and 8-camera motion capture system recorded limb kinematics. MatLab 2013b was used to calculate measures of gait stability: angular momentum, margin of stability, step width variability, CoM height, toe clearance, lateral arm swing. The minimum number of strides necessary to minimize intraparticipant variability was calculated via the interquartile range/median ratio (IMR) at 25% and 10% thresholds for each measure. A paired t test tested for significance between terrains (P < .05). The uneven walkway significantly destabilized gait as seen by increases in: coronal and sagittal plane angular momentum, step width variability, and toe clearance. We found no significant difference with the margin of stability between the 2 terrains possibly due to compensatory strategies (eg, lateral arm swing, trunk sway, step width). Recording a minimum of 10 strides per subject will keep each variable between the 25% and 10% IMR thresholds. In conclusion, the uneven walkway design significantly destabilizes human gait and at least 10 strides should be collected per subject.

Studies have demonstrated that individuals with chronic ankle instability (CAI) have diminished dynamic stability. Jerk-based measures have been utilized to examine dynamic balance because of their ability to quantify changes in acceleration and may provide an understanding of the postural corrections that occur during stabilizing following a jumping task. The purpose of this study was to compare acceleration and jerk following a jump stabilization task between individuals with CAI and the uninjured controls. Thirty-nine participants volunteered to participate in this case control study. Participants completed a jump stabilization task requiring them to jump off 2 feet, touch a marker set at 50% of their maximal vertical jump height, land on a single limb, and maintain balance for 3 seconds. Acceleration was calculated as the second derivative, and jerk was calculated as the third derivative of the displacement of the resultant vector position. Participants with CAI had greater acceleration (mean difference = 55.6 cm/s²; 95% confidence interval, 10.3 to 100.90; P = .017) and jerk compared with the uninjured controls (mean difference = 1804.5 cm/s³; 95% confidence interval, 98.7 to 3510.3; P = .039). These results suggest that individuals with CAI made faster and more frequent active postural control corrections to regain balance following a jump compared with the uninjured controls.