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.

Continuous human activity recognition for arbitrary directions with distributed radars / Guendel, R. G.; Unterhorst, M.; Gambi, E.; Fioranelli, F.; Yarovoy, A.. - ELETTRONICO. - 2021-:(2021), pp. 1-6. (Intervento presentato al convegno 2021 IEEE Radar Conference, RadarConf 2021 tenutosi a usa nel 2021) [10.1109/RadarConf2147009.2021.9454972].

Continuous human activity recognition for arbitrary directions with distributed radars

Unterhorst M.;Gambi E.;
2021-01-01

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.
2021
978-1-7281-7609-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/291877
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