Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/42982

TitleMUVTIME: a Multivariate time series visualizer for behavioral science
Author(s)Sousa, Emanuel Augusto Freitas
Malheiro, Tiago Emanuel Quintas
Bicho, Estela
Erlhagen, Wolfram
Santos, Jorge A.
Pereira, Alfredo F.
KeywordsMultivariate Time Series
Visualization
Cognition
Issue dateFeb-2016
Abstract(s)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-child dyads during joint action.
TypeConference paper
URIhttp://hdl.handle.net/1822/42982
DOI10.5220/0005725301650176
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CIPsi - Comunicações

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