#Burraco Software Contro Computer manual#

The reliance on video clips as stimulus materials presents researchers in sign language and gesture studies with a number of challenges. Alternatively, non-signers may use a wide variety of so-called co-speech gestures to support different communicative functions in situations where gestures are produced spontaneously alongside speech ( McNeill, 1985 Krauss, 1998 Özyürek, 2018).

Such gestures may be lexicalized signs of a natural sign language which can be combined to build up complex meanings ( Klima et al., 1979 Mathur and Rathmann, 2014 Cecchetto, 2017) and are primarily processed by the brain’s left-hemispheric core language network ( Emmorey, 2015 Trettenbrein et al., 2021).

#Burraco Software Contro Computer manual#

Researchers in linguistics, psychology, and neuroscience who are studying sign language and gesture frequently present their participants with pre-recorded video stimuli showing actors performing manual gestures.

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. 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. 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. 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. 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. 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.