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...

Full description

Saved in:
Bibliographic Details
Published in:The European journal of neuroscience 2021-12, Vol.54 (11), p.7946-7958
Main Author: Miyakoshi, Makoto
Format: Article
Language:English
Subjects:
Algorithms
audiomaze
Brain
Cognition
Computer Simulation
Human Body
Humans
Linear algebra
Missing data
Mobile brain‐body imaging
Motion
Motion capture
Movement
Neural networks
Neuroimaging
spline interpolation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0953-816X
1460-9568
DOI:10.1111/ejn.15531