Micro‐Doppler Radar‐Based Gait Classification of Common Pedestrians and Smartphone Zombies.

This paper proposes a micro‐Doppler radar‐based method to classify common pedestrians and pedestrians texting on a smartphone (called a smartphone zombies). We used a micro‐Doppler radar to collect motion data of the gait of participants texting on smartphones while walking, and then generated time‐frequency distribution images (spectrogram images). They were input into a convolutional neural network (CNN) to classify gait patterns. Results using measured data confirmed that the classification accuracy exceeded 90%, validating the effectiveness of the proposed method. Furthermore, by applying feature visualization by Grad‐Cam, we found that the motion of the leg swinging has essential features for the classification of smartphone zombies. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]