XMTC：伸手抓握手部运动学中多变量时间序列的可解释早期分类
XMTC: Explainable Early Classification of Multivariate Time Series in Reach-to-Grasp Hand Kinematics
AI总结：
本文提出XMTC工具，通过集成多机器学习方法和多视图可视化，在伸手抓握动作中实现多变量时间序列的可解释早期分类，并验证了其在真实HCI数据上的有效性。
摘要：
在人机交互（HCI）中，可以测量手部运动学，以预测用户在伸手抓握动作中的意图。通过使用多个手部传感器，可以捕获多变量时间序列数据。给定若干物体上的若干可能动作，目标是对多变量时间序列数据进行分类，其中类别应尽可能早地被预测出来。许多机器学习方法已被开发用于此类分类任务，其中不同的方法在不同的数据集上产生有利的解决方案。因此，我们采用一种集成方法，该方法包含不同的方法并对其进行加权。为了提供可信的分类结果，我们提出了XMTC工具，该工具结合了协调的多视图可视化来分析预测。时间精度图、混淆矩阵热图、时间置信度热图和部分依赖图允许识别早期预测与预测质量之间的最佳权衡，检测和分析具有挑战性的分类条件，并以概览和细节的方式研究预测演变。我们将XMTC应用于多个场景中的真实世界HCI数据，并表明使用我们的分类器可以在早期实现良好的分类预测，以及哪些条件容易区分，哪些多变量时间序列测量带来挑战，以及哪些特征影响最大。
英文摘要：
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. Using multiple hand sensors, multivariate time series data are being captured. Given a number of possible actions on a number of objects, the goal is to classify the multivariate time series data, where the class shall be predicted as early as possible. Many machine-learning methods have been developed for such classification tasks, where different approaches produce favorable solutions on different data sets. We, therefore, employ an ensemble approach that includes and weights different approaches. To provide a trustworthy classification production, we present the XMTC tool that incorporates coordinated multiple-view visualizations to analyze the predictions. Temporal accuracy plots, confusion matrix heatmaps, temporal confidence heatmaps, and partial dependence plots allow for the identification of the best trade-off between early prediction and prediction quality, the detection and analysis of challenging classification conditions, and the investigation of the prediction evolution in an overview and detail manner. We employ XMTC to real-world HCI data in multiple scenarios and show that good classification predictions can be achieved early on with our classifier as well as which conditions are easy to distinguish, which multivariate time series measurements impose challenges, and which features have most impact.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
