Human Activity Detection Using WiFi Signals and Deep Networks

This meetup was held in San Francisco on 6th February 2018.

Abstract:
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. Using DL models to detect human activity based on wifi signals is relatively recent activity. Though some probabilistic models were proposed in the past, the latest solutions reveal some exciting techniques. This discussion is intended for DL enthusiasts looking for “how” details. This talk may be useful for executives if they are curious about the “what” of the research.

Profile of the speaker:
SK Reddy is the Chief Product Officer AI & ML at Digitalist (www.digitalist.global). He is also a successful twice start-up entrepreneur and is an AI and ML expert. He is a frequent speaker at conferences and meetups. Additionally, he is an ML blogger.