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.

翻译：过去数十年间，人类行为的实时监测（尤其在电子健康应用中）一直是活跃的研究领域。在物联网感知环境的基础上，已有异常检测算法被提出用于早期识别异常行为。渐进式变化过程（通常称为漂移异常）在文献中受到的关注较少，因其比突发性临时变化（点异常）更具挑战性。本文首次提出一种完全无监督的实时漂移检测算法DynAmo，该算法能够在漂移发生时实时识别漂移周期。DynAmo包含两个核心组件：动态聚类组件用于捕获监测行为的整体趋势，轨迹生成组件则从最密集的聚类质心中提取特征。最终，我们通过滑动参考窗口与检测窗口上的分歧检验集成策略，对行为序列中的漂移周期进行检测。