基于自适应分组的反事实解释用于时间序列康复数据
Adaptive Group-Based Counterfactual Explanations for Time-Series Rehabilitation Data
AI总结：
针对多变量时间序列分类器在康复数据分析中难以解释的问题，提出两阶段框架，通过可学习门控机制生成基于语义特征组的反事实解释，在保持有效性的同时提升组级稀疏性和临床可解释性。
摘要：
多变量时间序列分类器的反事实解释在专家基于语义特征组而非单个通道推理的领域中往往难以解释。在采用多传感器惯性测量单元进行康复运动分析时，临床医生通过肌肉群和关节段抽象来解读运动；然而，现有的大多数反事实方法在通道级别操作，产生分散且生物力学上不连贯的解释。我们提出了一种两阶段框架，用于在高维IMU数据中生成基于组的反事实。我们首先证明Shapley自适应组排序保持了反事实有效性，但未能强制实现组级稀疏性，从而激发了对显式组选择的需求。然后，我们引入了可学习门控方法，该方法结合了可训练的每组相关性门控，与扰动掩码联合优化。在KneE-PAD康复数据集上的实验表明，与通道级M-CELS基线相比，LG在保持或提高有效性、时间平滑性和生成效率的同时，显著改善了模态组稀疏性。特定于运动的分析进一步表明，组结构反事实产生了简洁的、与临床推理一致的肌肉级纠正指导。总体而言，所提出的框架在不牺牲反事实质量的情况下增强了可解释性，为康复运动分析提供了更具可操作性的解释。
英文摘要：
Counterfactual explanations (CEs) for multivariate time-series classifiers are often difficult to interpret in domains where experts reason in terms of semantic feature groups rather than individual channels. In rehabilitation movement analysis with multi-sensor inertial measurement units (IMUs), clinicians interpret motion through muscle-group and joint-segment abstractions; yet, most existing counterfactual methods operate at the channel level, producing scattered and biomechanically incoherent explanations. We propose a two-stage framework for group-based counterfactual generation in high-dimensional IMU data. We first show that Shapley-Adaptive (SA) group ranking preserves counterfactual validity but fails to enforce group-level sparsity, motivating the need for explicit group selection. We then introduce Learnable Gate (LG) methods, which incorporate trainable per-group relevance gates jointly optimized with perturbation masks. Experiments on the KneE-PAD rehabilitation dataset demonstrate that LG substantially improves modality-group sparsity compared to the channel-level M-CELS baseline while maintaining or improving validity, temporal smoothness, and generation efficiency. Exercise-specific analyses further show that group-structured counterfactuals yield concise, muscle-level corrective guidance aligned with clinical reasoning. Overall, the proposed framework enhances interpretability without sacrificing counterfactual quality, enabling more actionable explanations for rehabilitation movement analysis.
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