Continuous human activity recognition for arbitrary directions with distributed radars
Ronny Guendel (TU Delft - Microwave Sensing, Signals & Systems)
M. Unterhorst (Università Politecnica delle Marche)
Ennio Gambi (Università Politecnica delle Marche)
F. Fioranelli (TU Delft - Microwave Sensing, Signals & Systems)
Alexander Yarovoy (TU Delft - Microwave Sensing, Signals & Systems)
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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