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A Novel Approach to Solve the “Missing Marker Problem” in Marker-Based Motion Analysis That Exploits the Segment Coordination Patterns in Multi-Limb Motion Data

Marker-based human motion analysis is an important tool in clinical research and in many practical applications. Missing marker information caused by occlusions or a marker falling off is a common problem impairing data quality. The current paper proposes a conceptually new gap filling algorithm and...

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Autor principal: Federolf, Peter Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3813748/
https://www.ncbi.nlm.nih.gov/pubmed/24205295
http://dx.doi.org/10.1371/journal.pone.0078689
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author Federolf, Peter Andreas
author_facet Federolf, Peter Andreas
author_sort Federolf, Peter Andreas
collection PubMed
description Marker-based human motion analysis is an important tool in clinical research and in many practical applications. Missing marker information caused by occlusions or a marker falling off is a common problem impairing data quality. The current paper proposes a conceptually new gap filling algorithm and presents results from a proof-of-principle analysis. The underlying idea of the proposed algorithm was that a multitude of internal and external constraints govern human motion and lead to a highly subject-specific movement pattern in which all motion variables are intercorrelated in a specific way. Two principal component analyses were used to determine how the coordinates of a marker with gaps correlated with the coordinates of the other, gap-free markers. Missing marker data could then be reconstructed through a series of coordinate transformations. The proposed algorithm was tested by reconstructing artificially created gaps in a 20-step walking trial and in an 18-s one-leg balance trial. The measurement accuracy’s dependence on the marker position, the length of the gap, and other parameters were evaluated. Even if only 2 steps of walking or 1.8 s of postural sway (10% of the whole marker data) were provided as input in the current study, the reconstructed marker trajectory differed on average no more than 11 mm from the originally measured trajectory. The reconstructed result improved further, on average, to distances below 5 mm if the marker trajectory was available more than 50% of the trial. The results of this proof-of-principle analysis supported the assumption that missing marker information can be reconstructed from the intercorrelations between marker coordinates, provided that sufficient data with complete marker information is available. Estimating missing information cannot be avoided entirely in many situations in human motion analysis. For some of these situations, the proposed reconstruction method may provide a better solution than what is currently available.
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spelling pubmed-38137482013-11-07 A Novel Approach to Solve the “Missing Marker Problem” in Marker-Based Motion Analysis That Exploits the Segment Coordination Patterns in Multi-Limb Motion Data Federolf, Peter Andreas PLoS One Research Article Marker-based human motion analysis is an important tool in clinical research and in many practical applications. Missing marker information caused by occlusions or a marker falling off is a common problem impairing data quality. The current paper proposes a conceptually new gap filling algorithm and presents results from a proof-of-principle analysis. The underlying idea of the proposed algorithm was that a multitude of internal and external constraints govern human motion and lead to a highly subject-specific movement pattern in which all motion variables are intercorrelated in a specific way. Two principal component analyses were used to determine how the coordinates of a marker with gaps correlated with the coordinates of the other, gap-free markers. Missing marker data could then be reconstructed through a series of coordinate transformations. The proposed algorithm was tested by reconstructing artificially created gaps in a 20-step walking trial and in an 18-s one-leg balance trial. The measurement accuracy’s dependence on the marker position, the length of the gap, and other parameters were evaluated. Even if only 2 steps of walking or 1.8 s of postural sway (10% of the whole marker data) were provided as input in the current study, the reconstructed marker trajectory differed on average no more than 11 mm from the originally measured trajectory. The reconstructed result improved further, on average, to distances below 5 mm if the marker trajectory was available more than 50% of the trial. The results of this proof-of-principle analysis supported the assumption that missing marker information can be reconstructed from the intercorrelations between marker coordinates, provided that sufficient data with complete marker information is available. Estimating missing information cannot be avoided entirely in many situations in human motion analysis. For some of these situations, the proposed reconstruction method may provide a better solution than what is currently available. Public Library of Science 2013-10-30 /pmc/articles/PMC3813748/ /pubmed/24205295 http://dx.doi.org/10.1371/journal.pone.0078689 Text en © 2013 Peter Andreas Federolf http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Federolf, Peter Andreas
A Novel Approach to Solve the “Missing Marker Problem” in Marker-Based Motion Analysis That Exploits the Segment Coordination Patterns in Multi-Limb Motion Data
title A Novel Approach to Solve the “Missing Marker Problem” in Marker-Based Motion Analysis That Exploits the Segment Coordination Patterns in Multi-Limb Motion Data
title_full A Novel Approach to Solve the “Missing Marker Problem” in Marker-Based Motion Analysis That Exploits the Segment Coordination Patterns in Multi-Limb Motion Data
title_fullStr A Novel Approach to Solve the “Missing Marker Problem” in Marker-Based Motion Analysis That Exploits the Segment Coordination Patterns in Multi-Limb Motion Data
title_full_unstemmed A Novel Approach to Solve the “Missing Marker Problem” in Marker-Based Motion Analysis That Exploits the Segment Coordination Patterns in Multi-Limb Motion Data
title_short A Novel Approach to Solve the “Missing Marker Problem” in Marker-Based Motion Analysis That Exploits the Segment Coordination Patterns in Multi-Limb Motion Data
title_sort novel approach to solve the “missing marker problem” in marker-based motion analysis that exploits the segment coordination patterns in multi-limb motion data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3813748/
https://www.ncbi.nlm.nih.gov/pubmed/24205295
http://dx.doi.org/10.1371/journal.pone.0078689
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