We consider the problem of ascertaining daily patterns using passive sensors to establish a baseline for elderly people living alone. The data are whether or not some movement, or human related activity, has occurred in the previous 15 min. We seek to segment the broad patterns within a day, for example, awake/sleep times or potentially more activity around meal-times. To address this problem we use changepoint detection which can segment the day into more/less active times. Traditional changepoint detection methods are inappropriate for these data as they fail to utilize the periodic nature of the data. The traditional assumption of conditional independence of the segments also hampers estimation of the within segment parameters. A new within-period changepoint detection scheme is proposed that instead assumes a circular perspective of the time axis. This permits the pooling of evidence of changepoint events from across multiple days. Inference is performed within the Bayesian framework by utilizing the reversible jump Markov chain Monte Carlo sampler to explore the variable dimension parameter space. Simulations demonstrate that the sampler achieves high accuracy in approximating the posterior while being able to detect small segments. Application to four individuals from our industrial collaborator provides insights to their daily patterns.

中文翻译：

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使用家庭活动传感器和期间变化点检测评估日常模式

我们考虑使用无源传感器确定日常模式的问题，以建立独居老年人的基线。数据是在前 15 分钟内是否发生了某些运动或人类相关活动。我们寻求在一天内细分广泛的模式，例如，清醒/睡眠时间或可能在进餐时间进行更多活动。为了解决这个问题，我们使用变更点检测，它可以将一天分成更多/更少的活动时间。传统的变化点检测方法不适合这些数据，因为它们无法利用数据的周期性。段的条件独立性的传统假设也妨碍了段内参数的估计。提出了一种新的周期内变化点检测方案，该方案采用时间轴的圆形视角。这允许汇集来自多天的变更点事件的证据。推理是在贝叶斯框架内通过利用可逆跳跃马尔可夫链蒙特卡罗采样器来探索变维参数空间来执行的。模拟表明，采样器在近似后验时实现了高精度，同时能够检测小片段。我们的工业合作者向四个人的应用提供了对他们日常模式的见解。推理是在贝叶斯框架内通过利用可逆跳跃马尔可夫链蒙特卡罗采样器来探索变维参数空间来执行的。模拟表明，采样器在近似后验时实现了高精度，同时能够检测小片段。我们的工业合作者向四个人的应用提供了对他们日常模式的见解。推理是在贝叶斯框架内通过利用可逆跳跃马尔可夫链蒙特卡罗采样器来探索变维参数空间来执行的。模拟表明，采样器在近似后验时实现了高精度，同时能够检测小片段。我们的工业合作者向四个人的应用提供了对他们日常模式的见解。