The promise of Mobile Health (mHealth) is the ability to use wearable sensors to monitor participant physiology at high frequencies during daily life to enable temporally-precise health interventions. However, a major challenge is frequent missing data. Despite a rich imputation literature, existing techniques are ineffective for the pulsative signals which comprise many mHealth applications, and a lack of available datasets has stymied progress. We address this gap with PulseImpute, the first large-scale pulsative signal imputation challenge which includes realistic mHealth missingness models, an extensive set of baselines, and clinically-relevant downstream tasks. Our baseline models include a novel transformer-based architecture designed to exploit the structure of pulsative signals. We hope that PulseImpute will enable the ML community to tackle this significant and challenging task.

翻译：移动健康（mHealth）的愿景在于利用可穿戴传感器在日常活动中高频监测参与者生理状态，从而实现时间精准的健康干预。然而，频繁的数据缺失构成了主要挑战。尽管已有丰富的插补研究文献，现有技术仍难以有效处理构成众多移动健康应用的脉动性信号，而可用数据集的匮乏也阻碍了相关进展。为弥补这一空白，我们提出"脉冲模拟"（PulseImpute）——首个大规模脉动性信号插补挑战任务，该任务包含符合实际的移动健康数据缺失模型、全面的基线方法体系以及具有临床相关性的下游任务。我们的基线模型包含一种新颖的基于Transformer的架构，该架构专门针对脉动性信号结构特征进行设计。我们期待脉冲模拟任务能推动机器学习社区攻克这一重要且极具挑战性的研究课题。