Estimation of human gait features by trajectory tracking and recombination using radar range‐Doppler‐time data

Abstract

Abstract Radar enables a convenient, contactless way for gait analysis in daily scenarios, such as in‐home health monitoring. However, the overlapping of human limb components could occur in both range and velocity dimensions of the radar echo, which makes it difficult to distinguish the limbs. In this study, a trajectory tracking and recombination method based on range‐Doppler‐time data is proposed for gait feature estimation under insufficient radar resolution. In the trajectory tracking step, the scattering amplitude is included for Kalman filtering, and the weighted joint nearest neighbour algorithm is proposed for data association. Then, the trajectories are recombined to construct the limb tracks based on the relative range‐Doppler angles. Finally, the limb tracks are used to estimate the spatial‐temporal and kinematic gait features. In our simulation, the trajectory tracking accuracies range from 98.2% to 99.6%. The gait feature estimation accuracies for simulation and measurement are no less than 91.9% and 93.8% respectively.

Keywords

• data association
• estimation theory
• human gait
• Kalman filter
• micro‐Doppler effect
• millimetre wave radar