arXiv Machine Learning By Andreas Spilz, Heiko Oppel, Michael Munz

Representing and Detecting Label Ambiguity in IMU-Based Exercise Evaluation

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arXiv:2607. 04842v1 Announce Type: new Abstract: Home-based physiotherapy is performed without supervision, which leads to incorrect execution and motivates systems that assess movement automatically from inertial measurement units (IMUs).

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

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.