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：在过去几十年中,对人的行为进行实时监测,特别是在电子保健应用方面,一直是一个积极的研究领域。除了基于IoT的遥感环境外,还提出了异常检测算法,以及早发现异常现象。文献中通常称为漂移异常的渐进变化程序受到的关注要少得多,因为它们代表着比突然临时变化(点异常)更具挑战性的情景。在本文中,我们首次提议采用完全不受监督的实时流动探测算法DynAmo,它可以识别正在发生的漂移期。DynAmo是一个动态的集群组件,以捕捉监测行为的总体趋势,一个轨迹生成元件,从最稠密的集聚体中提取特征。最后,我们对滑动参考和探测窗口进行一系列差异测试,以探测行为序列中的漂移期。