XMTC: Explainable Early Classification of Multivariate Time Series in Reach-to-Grasp Hand Kinematics

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dc.contributor.authorGol, Reyhaneh Sabbaghen_US
dc.contributor.authorValkov, Dimitaren_US
dc.contributor.authorLinsen, Larsen_US
dc.contributor.editorEgger, Bernharden_US
dc.contributor.editorGünther, Tobiasen_US
dc.date.accessioned2025-09-24T10:37:21Z
dc.date.available2025-09-24T10:37:21Z
dc.date.issued2025
dc.description.abstractUsing multiple hand sensors, hand kinematics can be measured in Human-Computer Interaction (HCI) with the intention to predict the user's intention in a reach-to-grasp action, leading to multivariate time series data. Then, the goal is to classify the multivariate time series data, where the class shall be predicted as early as possible. To investigate the prediction evolution, detect and analyze challenging conditions, and identify the best trade-off between early prediction and prediction quality, we present XMTC. XMTC incorporates visualizations on accuracy over time, multivariate time series classification probabilities, confusion matrices, and partial dependence plots for a trustworthy classification production. We employ XMTC to real-world HCI data in multiple scenarios to achieve good early classifications, as well as insights into which conditions are easy to distinguish, which multivariate time series measurements impose challenges, and which features have the most impact.en_US
dc.description.sectionheadersVisualization, Visual Analytics, and VR
dc.description.seriesinformationVision, Modeling, and Visualization
dc.identifier.doi10.2312/vmv.20251233
dc.identifier.isbn978-3-03868-294-3
dc.identifier.pages8 pages
dc.identifier.urihttps://doi.org/10.2312/vmv.20251233
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/vmv20251233
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleXMTC: Explainable Early Classification of Multivariate Time Series in Reach-to-Grasp Hand Kinematicsen_US
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