ABSTRACT
In order to provide effective test-retest and pooling of information from clinical gait analyses, it is critical to ensure that the data produced are as reliable as possible. Furthermore, it has been shown that anatomical marker placement is the largest source of inter-examiner variance in gait analyses. However, the effects of specific, known deviations in marker placement on calculated kinematic variables are unclear, and there is currently no mechanism to provide location-based feedback regarding placement consistency. The current study addresses these disparities by: applying a simulation of marker placement deviations to a large (n = 411) database of runners; evaluating a recently published method of morphometric-based deviation detection; and pilot-testing a system of location-based feedback for marker placements. Anatomical markers from a standing neutral trial were moved virtually by up to 30 mm to simulate deviations. Kinematic variables during running were then calculated using the original, and altered static trials. Results indicate that transverse plane angles at the knee and ankle are most sensitive to deviations in marker placement (7.59 degrees of change for every 10 mm of marker error), followed by frontal plane knee angles (5.17 degrees for every 10 mm). Evaluation of the deviation detection method demonstrated accuracies of up to 82% in classifying placements as deviant. Finally, pilot testing of a new methodology for providing location-based feedback demonstrated reductions of up to 80% in the deviation of outcome kinematics.
Subject(s)
Models, Theoretical , Running , Computer Simulation , HumansABSTRACT
As biomechanical research evolves, a continuing challenge is the standardization of data collection and analysis techniques. In gait analysis, placement of markers to construct an anatomical model has been identified as the single greatest source of error; however, there is currently no standardized approach to quantifying these errors. The current study applies morphometric methods, including a generalized Procrustes analysis (GPA) and a nearest neighbour comparison to quantify discrepancies in marker placement, with the goal of improving reliability in gait analysis. An extensive data-set collected by an Expert (n = 340) was used to evaluate marker placements performed by a Novice (n = 55). Variances identified through principal component analysis were used to create a modified GPA to transform anatomical data, and scaled coordinates from the Novice data-set were then scored against the Expert subset. The results showed quantitative differences in marker placement, suggesting that, although training improved consistency, systematic biases remained.
