RF-Sleep learns to predict sleep stages from radio measurements without any attached sensors on subjects.
We introduce a new predictive model that combines convolutional and recurrent neural networks to extract sleep-specific subject-invariant features
from RF signals and capture the temporal progression of sleep.
A key innovation underlying our approach is a modified adversarial training regime that discards extraneous information specific to individuals
or measurement conditions, while retaining all information relevant to the predictive task.