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.
We have openings for postdocs and interns! If you are interested in working on exciting projects like RF-Sleep, please send an email along with your resume to Prof. Dina Katabi at firstname.lastname@example.org.