Authors:
Emanuel Sousa
;
Tiago Malheiro
;
Estela Bicho
;
Wolfram Erlhagen
;
Jorge Santos
and
Alfredo Pereira
Affiliation:
University of Minho, Portugal
Keyword(s):
Multivariate Time Series, Visualization, Cognition.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
Interactive Visual Interfaces for Visualization
;
Interface and Interaction Techniques for Visualization
;
Visual Analytical Reasoning
;
Visual Data Analysis and Knowledge Discovery
;
Visual Representation and Interaction
Abstract:
As behavioral science becomes progressively more data driven, the need is increasing for appropriate tools for visual exploration and analysis of large datasets, often formed by multivariate time series. This paper describes MUVTIME, a multimodal time series visualization tool, developed in Matlab that allows a user to load a time series collection (a multivariate time series dataset) and an associated video. The user can plot several time series on MUVTIME and use one of them to do brushing on the displayed data, i.e. select a time range dynamically and have it updated on the display. The tool also features a categorical visualization of two binary time series that works as a high-level descriptor of the coordination between two interacting partners. The paper reports the successful use of MUVTIME under the scope of project TURNTAKE, which was intended to contribute to the improvement of human-robot interaction systems by studying turn-taking dynamics (role interchange) in parent-ch
ild dyads during joint action.
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