Anomalyq detection in Activities of Daily Living (ADL) plays an important role in e-health applications. An abrupt change in the ADL performed by a subject might indicate that she/he needs some help. Another important issue related with e-health applications is the case where the change in ADL undergoes a linear drift, which occurs in cognitive decline, Alzheimer’s disease or dementia. This work presents a novel method for detecting a linear drift in ADL modelled as circular normal distributions. The method is based on techniques commonly used in Statistical Process Control and, through the selection of a convenient threshold, is able to detect and estimate the change point in time when a linear drift started. Public datasets have been used to assess whether ADL can be modelled by a mixture of circular normal distributions. Exhaustive experimentation was performed on simulated data to assess the validity of the change detection algorithm, the results showing that the difference between the real change point and the estimated change point was \(4.90_{+3.17}^{-1.98}\) days on average. ADL can be modelled using a mixture of circular normal distributions. A new method to detect anomalies following a linear drift is presented. Exhaustive experiments showed that this method is able to estimate the change point in time for processes following a linear drift.

日常生活活动（ADL）中的异常检测在电子医疗应用中起着重要作用。受试者对ADL的突然更改可能表明她/她需要一些帮助。与电子医疗应用相关的另一个重要问题是ADL的变化呈线性漂移的情况，这种情况发生在认知能力下降，阿尔茨海默氏病或​​痴呆症中。这项工作提出了一种新方法，用于检测建模为圆形正态分布的ADL中的线性漂移。该方法基于统计过程控制中常用的技术，并且通过选择方便的阈值，能够检测和估计线性漂移开始时的时间变化点。公共数据集已用于评估ADL是否可以通过混合圆形正态分布进行建模。平均\（4.90 _ {+ 3.17} ^ {-1.98} \）天。可以使用圆形正态分布的混合对ADL进行建模。提出了一种检测线性漂移后异常的新方法。详尽的实验表明，该方法能够估计线性漂移后过程的时间变化点。