Adaptive Group-Based Counterfactual Explanations for Time-Series Rehabilitation Data

• URL: http://arxiv.org/abs/2607.01838v1
• Date: Thu, 02 Jul 2026 08:02:05 GMT
• Title: Adaptive Group-Based Counterfactual Explanations for Time-Series Rehabilitation Data
• Abstract summary: We propose a two-stage framework for group-based counterfactual generation in high-dimensional IMU data.<n>We first show that Shapley-Adaptive perturbation (SA) group ranking preserves counterfactual validity but fails to enforce group-level sparsity.<n>We then introduce Learnable Gate (LG) methods, which incorporate trainable per-group relevance gates jointly optimized with masks.
• Score: 17.023825093545582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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