Feature diversity for fall detection and human indoor activities classification using radar systems

Abstract

This paper presents a preliminary analysis of radar signatures for fall detection and classification of human indoor actions, to monitor the daily activity patterns of individuals at risk of deteriorating physical or cognitive health. Two datasets of signatures in different environments have been collected, one of which included signatures generated from signals simultaneously collected from a radar and an RGB-D Kinect sensor, on a couple of older individuals. This preliminary analysis shows the potential effectiveness of different features and classifiers, and highlights the need of additional investigation to exploit the diversity in terms of overall classification accuracy achieved with different features and classification methods, in different environments and datasets.