Bernard, JürgenJürgenBernardDobermann, EduardEduardDobermannBögl, MarkusMarkusBöglRöhlig, MartinMartinRöhligVögele, AnnaAnnaVögeleKohlhammer, JörnJörnKohlhammer2022-03-132022-03-132016https://publica.fraunhofer.de/handle/publica/39747110.2312/eurova.20161121Choosing appropriate time series segmentation algorithms and relevant parameter values is a challenging problem. In order to choose meaningful candidates it is important that different segmentation results are comparable. We propose a Visual Analytics (VA) approach to address these challenges in the scope of human motion capture data, a special type of multivariate time series data. In our prototype, users can interactively select from a rich set of segmentation algorithm candidates. In an overview visualization, the results of these segmentations can be compared and adjusted with regard to visualizations of raw data. A similarity-preserving colormap further facilitates visual comparison and labeling of segments. We present our prototype and demonstrate how it can ease the choice of winning candidates from a set of results for the segmentation of human motion capture data.eninformation visualizationVisual analyticstime series analysisdata miningmachine learningclusteringhuman motion analysisLead Topic: Digitized WorkLead Topic: Individual HealthLead Topic: Smart CityResearch Line: Computer graphics (CG)Research Line: Computer vision (CV)Research Line: Human computer interaction (HCI)conference paper