Feature diversity for fall detection and human indoor activities classification using radar systems - Shrestha, A; J. Le Kernec; Fioranelli, F; Cippitelli, E; Gambi, E; Spinsante, S (IET Conference Proceedings, 2017)
This paper presents preliminary analysis of radar signatures for fall detection and classification of human indoor activities, to monitor the daily behaviour 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 characterise and exploit the diversity of features and classification methods, in different experimental scenarios with different subjects.