Demo: Toward Continuous User Authentication Using PPG in Commodity Wrist-worn Wearables

We present a photoplethysmography (PPG)-based continuous user authentication (CA) system leveraging the pervasively equipped PPG sensor in commodity wrist-worn wearables such as the smartwatch. Compared to existing approaches, our system does not require any users' interactions (e.g., performing specific gestures) and is applicable to practical scenarios where the user's daily activities cause motion artifacts (MA). Notably, we design a robust MA removal method to mitigate the impact of MA. Furthermore, we explore the uniqueness of the human cardiac system and extract the fiducial features in the PPG measurements to train the gradient boosting tree (GBT) classifier, which can effectively differentiate users continuously using low training effort. In particular, we build the prototype of our system using a commodity smartwatch and a WebSocket server running on a laptop for CA. In order to demonstrate the practical use of our system, we will demo our prototype under different scenarios (i.e., static and moving) to show it can effectively detect MA caused by daily activities and achieve a high authentication success rate.