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Analysis of timing variability in human movements by aligning parameter curves in time

Overview of attention for article published in Behavior Research Methods, August 2017
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Title
Analysis of timing variability in human movements by aligning parameter curves in time
Published in
Behavior Research Methods, August 2017
DOI 10.3758/s13428-017-0952-0
Pubmed ID
Authors

Lisa K. Maurer, Heiko Maurer, Hermann Müller

Abstract

The analysis of timing in human movements requires a reference with which timing can be quantified. In reactive movements this reference is given by the stimulus. However, many movements do not respond to such an external event. In throwing, for instance, the hand opening for release has to be timed to an acceleration of the throwing arm. A common approach to analyzing release-timing variability is to choose a landmark in the movement that is supposed to have a fixed temporal relation to the release. Such distinct landmarks, however, are not always well definable. Therefore, the present article describes an alternative approach analyzing timing variability on the basis of the alignment of different trials relative to their kinematic shape, by shifting the trials in the time domain. The basic assumption behind this approach is that single throwing movements are one instance of an acquired movement template, and thus show a considerable similarity. In contrast, the location of the temporal moment of release varies from trial to trial, generating imprecision regarding the release timing. In trials synchronized with respect to the release, this variability can be assessed by shifting the kinematic profiles of the throwing movements in time such that they superimpose as closely as possible. As a result, the corresponding time shifts for all trials represent a measure of the release time deviations across trials, and the standard deviation of these deviations represents the timing variability. Aside from timing analyses in such movements as throwing, the approach can be applied to very different tasks with timing demands-for example, to neurophysiological signals.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 26%
Researcher 4 17%
Student > Master 3 13%
Lecturer 2 9%
Student > Bachelor 2 9%
Other 3 13%
Unknown 3 13%
Readers by discipline Count As %
Sports and Recreations 6 26%
Psychology 3 13%
Agricultural and Biological Sciences 2 9%
Engineering 2 9%
Nursing and Health Professions 1 4%
Other 4 17%
Unknown 5 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 September 2018.
All research outputs
#14,918,049
of 25,382,440 outputs
Outputs from Behavior Research Methods
#1,326
of 2,526 outputs
Outputs of similar age
#165,006
of 327,568 outputs
Outputs of similar age from Behavior Research Methods
#27
of 44 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,526 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
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We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.