Distributed Radar Information Fusion for Gait Recognition and Fall Detection

Abstract

This paper discusses a fusion framework with data from multiple, distributed radar sensors based on conventional classifiers, and transfer learning with pre-trained deep networks. The application considered is the classification of gait styles and the detection of critical accidents such as falls. The data were collected from a network comprised of one Ancortek frequency modulated continuous wave radar and three ultra wide-band Xethru radars. The radar systems within the network were placed in three different locations, notably, in front of participants, on the ceiling, and on the right-hand side of the monitored area. The proposed information fusion framework compares feature level fusion, soft fusion with the classifier confidence level, and hard fusion with Naïve Bayes combiner (NBC). Regarding the classifier, linear SVM, Random-Forest Bagging Trees, and five pre-trained neural networks are introduced to the fusion algorithm, where the VGG-16 network yields the best performance (about 84%) with the help of NBC. Compared to the best cases with conventional classifiers, it is reported that 20% and 16% subsequent improvement are achieved for individual usage of single radar and fusion