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This article studies how to detect and explain concept drift. Human activityrecognition is used as a case study together with a online batch learningsituation where the quality of the labels used in the model updating processstarts to decrease. Drift detection is based on identifying a set of featureshaving the largest relevance difference between the drifting model and a modelthat is known to be accurate and monitoring how the relevance of these featureschanges over time. As a main result of this article, it is shown that featurerelevance analysis cannot only be used to detect the concept drift but also toexplain the reason for the drift when a limited number of typical reasons forthe concept drift are predefined. To explain the reason for the concept drift,it is studied how these predefined reasons effect to feature relevance. Infact, it is shown that each of these has an unique effect to features relevanceand these can be used to explain the reason for concept drift.

Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition