  Wearable sensors enable health researchers to continuously collect data
pertaining to the physiological state of individuals in real-world settings.
However, such data can be subject to extensive missingness due to a complex
combination of factors. In this work, we study the problem of imputation of
missing step count data, one of the most ubiquitous forms of wearable sensor
data. We construct a novel and large scale data set consisting of a training
set with over 3 million hourly step count observations and a test set with over
2.5 million hourly step count observations. We propose a domain
knowledge-informed sparse self-attention model for this task that captures the
temporal multi-scale nature of step-count data. We assess the performance of
the model relative to baselines and conduct ablation studies to verify our
specific model designs.
