  We consider the problem of modeling cardiovascular responses to physical
activity and sleep changes captured by wearable sensors in free living
conditions. We use an attentional convolutional neural network to learn
parsimonious signatures of individual cardiovascular response from data
recorded at the minute level resolution over several months on a cohort of 80k
people. We demonstrate internal validity by showing that signatures generated
on an individual's 2017 data generalize to predict minute-level heart rate from
physical activity and sleep for the same individual in 2018, outperforming
several time-series forecasting baselines. We also show external validity
demonstrating that signatures outperform plain resting heart rate (RHR) in
predicting variables associated with cardiovascular functions, such as age and
Body Mass Index (BMI). We believe that the computed cardiovascular signatures
have utility in monitoring cardiovascular health over time, including detecting
abnormalities and quantifying recovery from acute events.
