Correcting whole-body motion capture data using rigid body transformation.

Using motion capture data as a part of mobile brain-body imaging (MoBI) recording has been increasing. With minimal linear algebra background, this paper explains how the rigid body transformation can be a useful preprocessing step for denoising and missing marker recovery. Such a transformation can provide insight and necessary-and-sufficient solutions requiring no assumption other than the minimum number of markers present. First, a simulation test using the empirical datasets from the AudioMaze project published from this journal's same volume demonstrates theoretical accuracy. The simulation results show that the rigid-body method perfectly recovers missing markers on a rigid body if a minimum of three marker positions is available. Second, the same transformation is applied to the empirical dataset. Before preprocessing, the raw data show that 15-80% of data frames had all markers present for rigid-body defined body parts. After using the rigid-body correction, most body parts recovered full markers in 90-95% of the data frames. The result also suggests the necessity for performing across-trial corrections for within-participant (42% missing detected in one of the body parts) and across-participants (11% missing). The discussion section introduces a solution and a performance summary for non-rigid-body marker correction using a neural network. Data support that the rigid body transformation is an intuitive and powerful correction method necessary for preprocessing motion capture data for neurocognitive experiments. The supporting information section contains a URL link to Matlab code and example data.