Presented by SK Reddy, Chief Product Officer AI at Hexagon Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ Detecting indoor human activity is used for security, patient care, baby monitoring, etc. purposes. Other than having another human being providing the service (i.e. a security guard, a nurse, baby’s mother, etc.), many solutions have been suggested using image processing neural networks that detect patient’s fall, baby walking, door open, etc. Many of these models have achieved higher prediction accuracy rates. But neural networks that use video cameras bring up privacy concerns. Custom made sensors, though solve the problem, are expensive. Researchers have proposed deep learning (DL) models use wifi signals to detect human activity. This is relatively recent research. I would like to discuss on how to design a DL to detect human activity to use Wifi signals that are available from off-the-shelf wifi routers. I will also discuss the architecture of such models, share the implementation problems and evaluate solutions that may address these problems.