Real-Time Motion Capture Toolbox (RTMocap): ...
Document type :
Article dans une revue scientifique
PMID :
Permalink :
Title :
Real-Time Motion Capture Toolbox (RTMocap): an open-source code for recording 3-D motion kinematics to study action-effect anticipations during motor and social interactions
Author(s) :
Lewkowicz, Daniel [Auteur]
Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 [SCALab]
Delevoye, Yvonne [Auteur]
Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 [SCALab]
Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 [SCALab]
Delevoye, Yvonne [Auteur]

Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 [SCALab]
Journal title :
Behavior Research Methods
Abbreviated title :
Behav Res Methods
Volume number :
48
Pages :
366-380
Publication date :
2016-03
ISSN :
1554-3528
HAL domain(s) :
Sciences cognitives
English abstract : [en]
We present here a toolbox for the real-time motion capture of biological movements that runs in the cross-platform MATLAB environment (The MathWorks, Inc., Natick, MA). It provides instantaneous processing of the 3-D ...
Show more >We present here a toolbox for the real-time motion capture of biological movements that runs in the cross-platform MATLAB environment (The MathWorks, Inc., Natick, MA). It provides instantaneous processing of the 3-D movement coordinates of up to 20 markers at a single instant. Available functions include (1) the setting of reference positions, areas, and trajectories of interest; (2) recording of the 3-D coordinates for each marker over the trial duration; and (3) the detection of events to use as triggers for external reinforcers (e.g., lights, sounds, or odors). Through fast online communication between the hardware controller and RTMocap, automatic trial selection is possible by means of either a preset or an adaptive criterion. Rapid preprocessing of signals is also provided, which includes artifact rejection, filtering, spline interpolation, and averaging. A key example is detailed, and three typical variations are developed (1) to provide a clear understanding of the importance of real-time control for 3-D motion in cognitive sciences and (2) to present users with simple lines of code that can be used as starting points for customizing experiments using the simple MATLAB syntax. RTMocap is freely available (http://sites.google.com/site/RTMocap/) under the GNU public license for noncommercial use and open-source development, together with sample data and extensive documentation.Show less >
Show more >We present here a toolbox for the real-time motion capture of biological movements that runs in the cross-platform MATLAB environment (The MathWorks, Inc., Natick, MA). It provides instantaneous processing of the 3-D movement coordinates of up to 20 markers at a single instant. Available functions include (1) the setting of reference positions, areas, and trajectories of interest; (2) recording of the 3-D coordinates for each marker over the trial duration; and (3) the detection of events to use as triggers for external reinforcers (e.g., lights, sounds, or odors). Through fast online communication between the hardware controller and RTMocap, automatic trial selection is possible by means of either a preset or an adaptive criterion. Rapid preprocessing of signals is also provided, which includes artifact rejection, filtering, spline interpolation, and averaging. A key example is detailed, and three typical variations are developed (1) to provide a clear understanding of the importance of real-time control for 3-D motion in cognitive sciences and (2) to present users with simple lines of code that can be used as starting points for customizing experiments using the simple MATLAB syntax. RTMocap is freely available (http://sites.google.com/site/RTMocap/) under the GNU public license for noncommercial use and open-source development, together with sample data and extensive documentation.Show less >
Language :
Anglais
Audience :
Non spécifiée
Administrative institution(s) :
Université de Lille
CNRS
CHU Lille
CNRS
CHU Lille
Research team(s) :
Équipe Action, Vision et Apprentissage (AVA)
Submission date :
2019-02-13T14:48:20Z
2020-04-08T11:29:25Z
2020-04-08T11:29:25Z