Skip to yearly menu bar Skip to main content


Poster

PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation

Maxwell Xu · Alexander Moreno · Supriya Nagesh · Varol Aydemir · David Wetter · Santosh Kumar · James Rehg

Hall J (level 1) #1022

Keywords: [ self-attention ] [ dataset ] [ imputation ] [ time-series ] [ pulsative ] [ physiological ] [ sensors ] [ mHealth ] [ missingness ]


Abstract:

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 important and challenging task.

Chat is not available.