Mixture of hidden Markov models for accelerometer data
Mixture of hidden Markov models for accelerometer data
复制标题
加速度计数据的隐马尔可夫模型的混合
DOI：
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发表时间：
2019
影响因子：
1.8
通讯作者：
F. Navarro
中科院分区：
文献类型：
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作者：
M. D. R. D. Chaumaray;M. Marbac;F. Navarro
在对加速度计数据分析的激励下，我们引入了具有特定特征的隐马尔可夫模型的特定有限混合模型，该模型很好地适应了这类数据的特定性质。我们的模型允许计算统计数据，以表征受试者的身体活动（\emph{例如}，在不同活动水平上花费的平均时间和在两个活动水平之间转换的概率），而无需事先指定活动水平，而是通过从数据中估计它们。此外，这种方法可以考虑到人群的异质性，并定义具有均匀体育活动行为的亚人群。
我们证明，在温和的假设下，该模型表明，错误分类的概率随着其测量序列的长度呈指数衰减下降。模型的可识别性也进行了研究。我们还报告了一套全面的数值模拟来支持我们的理论发现。该方法是由PAT研究激发并应用于PAT研究的。
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.
影响因子：
5.6
作者：
Pollak, CP;Tryon, WW;Dzwonczyk, R
通讯作者：
Dzwonczyk, R
影响因子：
5
作者：
Lim, Sungwoo;Wyker, Brett;Eisenhower, Donna
通讯作者：
Eisenhower, Donna