Assessing daily patterns using home activity sensors and within period changepoint detection
Assessing daily patterns using home activity sensors and within period changepoint detection
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DOI：
10.1111/rssc.12472
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发表时间：
2021-02-24
影响因子：
1.6
通讯作者：
Rogerson, Louise
中科院分区：
文献类型：
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作者：
Taylor, Simon A. C.;Killick, Rebecca;Rogerson, Louise
我们考虑使用无源传感器确定日常模式的问题，为独居老年人建立基线。这些数据是在过去 15 分钟内是否发生了某些移动或人类相关活动。我们力求对一天内的主要模式进行细分，例如清醒/睡眠时间或进餐时间附近可能进行的更多活动。为了解决这个问题，我们使用变化点检测，它可以将一天划分为更多/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特性。分段条件独立性的传统假设也妨碍了分段内参数的估计。提出了一种新的周期内变化点检测方案，该方案假设时间轴的圆形视角。这允许汇集多天内的变化点事件的证据。利用可逆跳跃马尔可夫链蒙特卡罗采样器在贝叶斯框架内进行推理，探索可变维度参数空间。仿真表明，采样器在近似后验方面实现了高精度，同时能够检测小片段。我们的工业合作伙伴对四位人士的应用提供了对他们日常模式的见解。
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.