DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks

ArXi:2605.31007v1 Announce Type: cross Anomaly detection in physiological sensor data from Wireless Body Area Networks (WBANs) can be caused by sensor faults, network disruptions, or missing data, leading to false alarms. Hence, it demands both high predictive accuracy and clinically interpretable explanations. Existing approaches rely either on black-box models that achieve strong performance but offer no transparency, or on post-prediction explanation methods such as SHAP and