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The promise of Mobile Health (mHealth) is the ability to use wearable sensorsto monitor participant physiology at high frequencies during daily life toenable temporally-precise health interventions. However, a major challenge isfrequent missing data. Despite a rich imputation literature, existingtechniques are ineffective for the pulsative signals which comprise manymHealth applications, and a lack of available datasets has stymied progress. Weaddress this gap with PulseImpute, the first large-scale pulsative signalimputation challenge which includes realistic mHealth missingness models, anextensive set of baselines, and clinically-relevant downstream tasks. Ourbaseline models include a novel transformer-based architecture designed toexploit the structure of pulsative signals. We hope that PulseImpute willenable the ML community to tackle this significant and challenging task.

PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation