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

DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks arXiv:2605.31007v1 Announce Type: cross Abstract: 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