Unsupervised Statistical Concept Drift Detection for Behaviour Abnormality Detection

Abnormal behaviour can be an indicator for a medical condition in older adults. Our novel unsupervised statistical concept drift detection approach uses variational autoencoders for estimating the parameters for a statistical hypothesis test for abnormal days. As feature, the Kullback–Leibler divergence of activity probability maps derived from power and motion sensors were used. We showed the general feasibility (min. F1-Score of 91%) on an artificial dataset of four concept drift types. Then we applied our new method to our real–world dataset collected from the homes of 20 (pre–)frail older adults (avg. age 84.75y). Our method was able to find abnormal days when a participant suffered from severe medical condition.