arXiv:2607.01838v1 Announce Type: new 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
The increasing sophistication of AI models and the critical need for explainability in sensitive domains like healthcare are driving this research, particularly as time-series data becomes more prevalent.
This development can significantly enhance the adoption and trustworthiness of AI in medical rehabilitation by making explanations comprehensible to human experts, bridging the gap between AI outputs and clinical reasoning.
The ability to generate counterfactual explanations that align with semantic group-based reasoning, rather than individual sensor channels, improves the clinical utility and interpretability of AI in rehabilitation.
- · AI in healthcare
- · Rehabilitation clinics
- · Medical AI developers
- · Patients
- · Black-box AI models in medicine
Improved trust and adoption of AI in rehabilitation movement analysis.
Expansion of similar explainable AI techniques to other complex time-series data domains with expert group reasoning.
Accelerated integration of AI-driven diagnostics and personalized treatment plans across various medical specialties due to enhanced interpretability.
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Read at arXiv cs.LG