Mobile data technologies use ``actigraphs'' to furnish information on health variables as a function of a subject's movement. The advent of wearable devices and related technologies has propelled the creation of health databases consisting of human movement data to conduct research on mobility patterns and health outcomes. Statistical methods for analyzing high-resolution actigraph data depend on the specific inferential context, but the advent of Artificial Intelligence (AI) frameworks require that the methods be congruent to transfer learning and amortization. This article devises amortized Bayesian inference for actigraph time sheets. We pursue a Bayesian approach to ensure full propagation of uncertainty and its quantification using a hierarchical dynamic linear model. We build our analysis around actigraph data from the Physical Activity through Sustainable Transport Approaches in Los Angeles (PASTA-LA) study conducted by the Fielding School of Public Health in the University of California, Los Angeles. Apart from achieving probabilistic imputation of actigraph time sheets, we are also able to statistically learn about the time-varying impact of explanatory variables on the magnitude of acceleration (MAG) for a cohort of subjects.

翻译：移动数据技术利用"活动记录仪"提供与受试者运动相关的健康变量信息。可穿戴设备及相关技术的兴起推动了由人体运动数据构成的健康数据库建设，用于研究活动模式与健康结果。分析高分辨率活动记录数据的统计方法取决于具体推断情境，但人工智能框架的出现要求这些方法需兼容迁移学习与摊销机制。本文针对活动记录时间表设计了摊销贝叶斯推断方法。我们采用贝叶斯框架以确保不确定性的完整传播，并利用分层动态线性模型进行量化。研究以加州大学洛杉矶分校菲尔丁公共卫生学院开展的"洛杉矶可持续交通方式体力活动研究"中的活动记录数据为核心分析对象。除实现活动记录时间表的概率插补外，我们还能通过统计学习揭示解释变量对受试者队列加速度幅值随时间变化的动态影响。