加速度计数据的混合隐马尔可夫模型
Mixture of hidden Markov models for accelerometer data
摘要：
出于对加速度计数据的分析，我们引入了一个特定的有限混合的隐马尔可夫模型，具有特殊的特点，很好地适应这种类型的数据的特定性质。我们的模型允许计算表征受试者身体活动的统计数据（\emph{e.g.}，在不同活动水平上花费的平均时间和两个活动水平之间的转换概率），而无需预先指定活动水平，而是通过从数据中估计它们。此外，这种方法允许考虑人群的异质性，并定义具有同质体力活动行为的亚群。我们证明了，在温和的假设下，这个模型意味着错误分类的主题的概率下降的指数衰减与其测量序列的长度。模型的可辨识性也进行了研究。我们还报告了一套全面的数值模拟，以支持我们的理论研究结果。方法的动机和应用到PAT研究。
英文摘要：
Motivated by the analysis of accelerometer data, we introduce a specific finite mixture of hidden Markov models with particular characteristics that adapt well to the specific nature of this type of data. Our model allows for the computation of statistics that characterize the physical activity of a subject (\emph{e.g.}, the mean time spent at different activity levels and the probability of the transition between two activity levels) without specifying the activity levels in advance but by estimating them from the data. In addition, this approach allows the heterogeneity of the population to be taken into account and subpopulations with homogeneous physical activity behavior to be defined. We prove that, under mild assumptions, this model implies that the probability of misclassifying a subject decreases at an exponential decay with the length of its measurement sequence. Model identifiability is also investigated. We also report a comprehensive suite of numerical simulations to support our theoretical findings. Method is motivated by and applied to the PAT study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
