  The prompt and accurate recognition of Continuous Human Activity (CHAR) is
critical in identifying and responding to health events, particularly fall risk
assessment. In this paper, we examine a multi-antenna radar system that can
process radar data returns for multiple individuals in an indoor setting,
enabling CHAR for multiple subjects. This requires combining spatial and
temporal signal processing techniques through micro-Doppler (MD) analysis and
high-resolution receive beamforming. We employ delay and sum beamforming to
capture MD signatures at three different directions of observation. As MD
images may contain multiple activities, we segment the three MD signatures
using an STA/LTA algorithm. MD segmentation ensures that each MD segment
represents a single human motion activity. Finally, the segmented MD image is
resized and processed through a convolutional neural network (CNN) to classify
motion against each MD segment.
