English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Controlling video stimuli in sign language and gesture research: The OpenPoseR package for analyzing OpenPose motion tracking data in R

MPS-Authors
/persons/resource/persons213900

Trettenbrein,  Patrick
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons23567

Zaccarella,  Emiliano
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

External Ressource
No external resources are shared
Fulltext (public)

Trettenbrein_Zaccarella_2021.pdf
(Publisher version), 581KB

Supplementary Material (public)
There is no public supplementary material available
Citation

Trettenbrein, P., & Zaccarella, E. (2021). Controlling video stimuli in sign language and gesture research: The OpenPoseR package for analyzing OpenPose motion tracking data in R. Frontiers in Psychology, 12: 628728. doi:10.3389/fpsyg.2021.628728.


Cite as: http://hdl.handle.net/21.11116/0000-0007-DD8C-3
Abstract
Researchers in the fields of sign language and gesture studies frequently present their participants with video stimuli showing actors performing linguistic signs or co-speech gestures. Up to now, such video stimuli have been mostly controlled only for some of the technical aspects of the video material (e.g., duration of clips, encoding, framerate, etc.), leaving open the possibility that systematic differences in video stimulus materials may be concealed in the actual motion properties of the actor’s movements. Computer vision methods such as OpenPose enable the fitting of body-pose models to the consecutive frames of a video clip and thereby make it possible to recover the movements performed by the actor in a particular video clip without the use of a point-based or markerless motion-tracking system during recording. The OpenPoseR package provides a straightforward and reproducible way of working with these body-pose model data extracted from video clips using OpenPose, allowing researchers in the fields of sign language and gesture studies to quantify the amount of motion (velocity and acceleration) pertaining only to the movements performed by the actor in a video clip. These quantitative measures can be used for controlling differences in the movements of an actor in stimulus video clips or, for example, between different conditions of an experiment. In addition, the package also provides a set of functions for generating plots for data visualization, as well as an easy-to-use way of automatically extracting metadata (e.g., duration, framerate, etc.) from large sets of video files.