Human Activity Detection Using WiFi Signals and Deep Networks | San Francisco

About this event

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, and others. 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.

In this session, SK Reddy will discuss how to design a deep learning model to detect human activity to use Wifi signals that are available from off-the- shelf wifi routers. He will also discuss the architecture of such models, share the implementation problems and evaluate solutions that may address these problems.

This discussion is geared towards DL enthusiasts looking for “how” details and executives who are curious about the “what” of the research.