Identification of Smartphone Zombies and Normal Pedestrians Using FMCW Radar and Machine Learning

Abstract

Mobile phone usage represents a source of distraction for pedestrians, who are losing awareness of external hazards given by vehicles and environment. Radars could be a solution to monitor continuously and privately the behaviours of pedestrians in the main public spaces in order to find solutions based on the way pedestrians walk, their habits and their walking speed. Being able to identify a pedestrian with the head down on the phone, usually called “smartphone zombie’’, is crucial to intervene to make the road safer and discourage the behaviour. We study the feasibility of identifying the walking pattern of “smartphone zombie’’ against a control pedestrian walking normally exploiting an automotive frequency modulated continuous wave radar working at 77 GHz. By applying principal component analysis and machine learning we obtain a classification accuracy of 92.4% of smartphone zombies against normal walk and 87.6% when adding a third class of fast walkers.