Continuous Human Activity Recognition and Step-Time Variability Analysis with FMCW Radar

Human activity recognition (HAR) and gait analysis are important functions that support aging-in-place and remote health monitoring. Although there have been many works investigating HAR with radar based on single-activity snapshots in time, few works address recognition in continuous streams of radio frequency (RF) data, where in daily life many different activities are conducted. This work proposes a novel variable window averaging method to segment RF data streams containing a mixture of large-scale gross motor activities as well as fine-grain hand gestures, a physics-aware generative adversarial network (PhGAN) to recognize daily activities, and a new technique to estimate step-time variability from RF data. Our results show that extraction of motion detected intervals and GAN-synthesized samples during training boosts HAR accuracy, while the estimation variance of time-step variability from radar compares well with that obtained from a Vicon motion capture system.