A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment

Justin Baudisch; Birte Richter; Thorsten Jungeblut

doi:10.1007/s13218-022-00775-5

A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment

Kunstliche Intell (Oldenbourg). 2022;36(3-4):259-266. doi: 10.1007/s13218-022-00775-5. Epub 2022 Oct 31.

Authors

Justin Baudisch¹, Birte Richter², Thorsten Jungeblut¹

Affiliations

¹ Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, Bielefeld, Germany.
² Medical Assistance Systems, Medical School OWL, Center for Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany.

Abstract

This paper presents a framework for learning event sequences for anomaly detection in a smart home environment. It addresses environment conditions, device grouping, system performance and explainability of anomalies. Our method models user behavior as sequences of events, triggered by interaction of the home residents with the Internet of Things (IoT) devices. Based on a given set of recorded event sequences, the system can learn the habitual behavior of the residents. An anomaly is described as deviation from that normal behavior, previously learned by the system. One key feature of our framework is the explainability of detected anomalies, which is implemented through a simple rule analysis.

Keywords: Ambient assisted living; Anomaly detection; Explainable AI; Internet of things.