Continuous human activity recognition for arbitrary directions with distributed radars
Ronny G. Guendel, Matteo Unterhorst, Ennio Gambi, Francesco Fioranelli, Alexander Yarovoy
Abstract
Continuous Activities of Daily Living (ADL) recognition in an arbitrary movement direction using five distributed pulsed Ultra-Wideband (UWB) radars in a coordinated network is proposed. Classification approaches in unconstrained activity trajectories that render a more natural occurrence for Human Activity Recognition (HAR) are investigated. Feature and decision fusion methods are applied to the priorly extracted handcrafted features from the range-Doppler. A following multi-nomial logistic regression classifier, commonly known as Softmax, provides explicit probabilities associated with each target label. The outputs of these classifiers from different radar nodes were combined with a probability prediction balancing approach over time to improve performances. The final results show average improvements between 6.8% and 17.5% compared to the usage of any single radar in unconstrained directions.