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This paper introduces a two-stage framework for generating adaptive group-based counterfactual explanations (CEs) tailored for multivariate time-series data in rehabilitation movement analysis. By employing Shapley-Adaptive group ranking and Learnable Gate methods, the authors achieve improved modality-group sparsity while ensuring counterfactual validity and temporal smoothness. Experiments on the KneE-PAD rehabilitation dataset reveal that the proposed approach yields more interpretable and clinically relevant explanations compared to traditional channel-level methods, enhancing the utility of CEs in clinical settings.
Group-structured counterfactuals provide concise, muscle-level guidance that aligns with clinical reasoning, transforming how rehabilitation data is interpreted.
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.
