Separating timing, movement conditions and individual differences in the analysis of human movement

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Standard

Separating timing, movement conditions and individual differences in the analysis of human movement. / Raket, Lars Lau; Grimme, Britta; Schöner, Gregor; Igel, Christian; Markussen, Bo.

I: PLoS Computational Biology, Bind 12, Nr. 9, 2016, s. e1005092.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Raket, LL, Grimme, B, Schöner, G, Igel, C & Markussen, B 2016, 'Separating timing, movement conditions and individual differences in the analysis of human movement', PLoS Computational Biology, bind 12, nr. 9, s. e1005092. https://doi.org/10.1371/journal.pcbi.1005092

APA

Raket, L. L., Grimme, B., Schöner, G., Igel, C., & Markussen, B. (2016). Separating timing, movement conditions and individual differences in the analysis of human movement. PLoS Computational Biology, 12(9), e1005092. https://doi.org/10.1371/journal.pcbi.1005092

Vancouver

Raket LL, Grimme B, Schöner G, Igel C, Markussen B. Separating timing, movement conditions and individual differences in the analysis of human movement. PLoS Computational Biology. 2016;12(9):e1005092. https://doi.org/10.1371/journal.pcbi.1005092

Author

Raket, Lars Lau ; Grimme, Britta ; Schöner, Gregor ; Igel, Christian ; Markussen, Bo. / Separating timing, movement conditions and individual differences in the analysis of human movement. I: PLoS Computational Biology. 2016 ; Bind 12, Nr. 9. s. e1005092.

Bibtex

@article{5a99ef2af97847d39f353b65fd46b85c,
title = "Separating timing, movement conditions and individual differences in the analysis of human movement",
abstract = "A central task in the analysis of human movement behavior is to determine systematic patterns and differences across experimental conditions, participants and repetitions. This is possible because human movement is highly regular, being constrained by invariance principles. Movement timing and movement path, in particular, are linked through scaling laws. Separating variations of movement timing from the spatial variations of movements is a well-known challenge that is addressed in current approaches only through forms of preprocessing that bias analysis. Here we propose a novel nonlinear mixed-effects model for analyzing temporally continuous signals that contain systematic effects in both timing and path. Identifiability issues of path relative to timing are overcome by using maximum likelihood estimation in which the most likely separation of space and time is chosen given the variation found in data. The model is applied to analyze experimental data of human arm movements in which participants move a hand-held object to a target location while avoiding an obstacle. The model is used to classify movement data according to participant. Comparison to alternative approaches establishes nonlinear mixed-effects models as viable alternatives to conventional analysis frameworks. The model is then combined with a novel factor-analysis model that estimates the low-dimensional subspace within which movements vary when the task demands vary. Our framework enables us to visualize different dimensions of movement variation and to test hypotheses about the effect of obstacle placement and height on the movement path. We demonstrate that the approach can be used to uncover new properties of human movement.",
author = "Raket, {Lars Lau} and Britta Grimme and Gregor Sch{\"o}ner and Christian Igel and Bo Markussen",
year = "2016",
doi = "10.1371/journal.pcbi.1005092",
language = "English",
volume = "12",
pages = "e1005092",
journal = "P L o S Computational Biology (Online)",
issn = "1553-7358",
publisher = "Public Library of Science",
number = "9",

}

RIS

TY - JOUR

T1 - Separating timing, movement conditions and individual differences in the analysis of human movement

AU - Raket, Lars Lau

AU - Grimme, Britta

AU - Schöner, Gregor

AU - Igel, Christian

AU - Markussen, Bo

PY - 2016

Y1 - 2016

N2 - A central task in the analysis of human movement behavior is to determine systematic patterns and differences across experimental conditions, participants and repetitions. This is possible because human movement is highly regular, being constrained by invariance principles. Movement timing and movement path, in particular, are linked through scaling laws. Separating variations of movement timing from the spatial variations of movements is a well-known challenge that is addressed in current approaches only through forms of preprocessing that bias analysis. Here we propose a novel nonlinear mixed-effects model for analyzing temporally continuous signals that contain systematic effects in both timing and path. Identifiability issues of path relative to timing are overcome by using maximum likelihood estimation in which the most likely separation of space and time is chosen given the variation found in data. The model is applied to analyze experimental data of human arm movements in which participants move a hand-held object to a target location while avoiding an obstacle. The model is used to classify movement data according to participant. Comparison to alternative approaches establishes nonlinear mixed-effects models as viable alternatives to conventional analysis frameworks. The model is then combined with a novel factor-analysis model that estimates the low-dimensional subspace within which movements vary when the task demands vary. Our framework enables us to visualize different dimensions of movement variation and to test hypotheses about the effect of obstacle placement and height on the movement path. We demonstrate that the approach can be used to uncover new properties of human movement.

AB - A central task in the analysis of human movement behavior is to determine systematic patterns and differences across experimental conditions, participants and repetitions. This is possible because human movement is highly regular, being constrained by invariance principles. Movement timing and movement path, in particular, are linked through scaling laws. Separating variations of movement timing from the spatial variations of movements is a well-known challenge that is addressed in current approaches only through forms of preprocessing that bias analysis. Here we propose a novel nonlinear mixed-effects model for analyzing temporally continuous signals that contain systematic effects in both timing and path. Identifiability issues of path relative to timing are overcome by using maximum likelihood estimation in which the most likely separation of space and time is chosen given the variation found in data. The model is applied to analyze experimental data of human arm movements in which participants move a hand-held object to a target location while avoiding an obstacle. The model is used to classify movement data according to participant. Comparison to alternative approaches establishes nonlinear mixed-effects models as viable alternatives to conventional analysis frameworks. The model is then combined with a novel factor-analysis model that estimates the low-dimensional subspace within which movements vary when the task demands vary. Our framework enables us to visualize different dimensions of movement variation and to test hypotheses about the effect of obstacle placement and height on the movement path. We demonstrate that the approach can be used to uncover new properties of human movement.

U2 - 10.1371/journal.pcbi.1005092

DO - 10.1371/journal.pcbi.1005092

M3 - Journal article

C2 - 27657545

VL - 12

SP - e1005092

JO - P L o S Computational Biology (Online)

JF - P L o S Computational Biology (Online)

SN - 1553-7358

IS - 9

ER -

ID: 166095242