XMTC: Explainable Early Classification of Multivariate Time Series in Reach-to-Grasp Hand Kinematics
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Using 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.
Description
@inproceedings{10.2312:vmv.20251233,
booktitle = {Vision, Modeling, and Visualization},
editor = {Egger, Bernhard and Günther, Tobias},
title = {{XMTC: Explainable Early Classification of Multivariate Time Series in Reach-to-Grasp Hand Kinematics}},
author = {Gol, Reyhaneh Sabbagh and Valkov, Dimitar and Linsen, Lars},
year = {2025},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {10.2312/vmv.20251233}
}