  Real-time monitoring of human behaviours, especially in e-Health
applications, has been an active area of research in the past decades. On top
of IoT-based sensing environments, anomaly detection algorithms have been
proposed for the early detection of abnormalities. Gradual change procedures,
commonly referred to as drift anomalies, have received much less attention in
the literature because they represent a much more challenging scenario than
sudden temporary changes (point anomalies). In this paper, we propose, for the
first time, a fully unsupervised real-time drift detection algorithm named
DynAmo, which can identify drift periods as they are happening. DynAmo
comprises a dynamic clustering component to capture the overall trends of
monitored behaviours and a trajectory generation component, which extracts
features from the densest cluster centroids. Finally, we apply an ensemble of
divergence tests on sliding reference and detection windows to detect drift
periods in the behavioural sequence.
