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We propose HAROOD as a short-range FMCW radar-based human activity classifierand out-of-distribution (OOD) detector. It aims to classify human sitting,standing, and walking activities and to detect any other moving or stationaryobject as OOD. We introduce a two-stage network. The first stage is trainedwith a novel loss function that includes intermediate reconstruction loss,intermediate contrastive loss, and triplet loss. The second stage uses thefirst stage's output as its input and is trained with cross-entropy loss. Itcreates a simple classifier that performs the activity classification. On ourdataset collected by 60 GHz short-range FMCW radar, we achieve an averageclassification accuracy of 96.51%. Also, we achieve an average AUROC of 95.04%as an OOD detector. Additionally, our extensive evaluations demonstrate thesuperiority of HAROOD over the state-of-the-art OOD detection methods in termsof standard OOD detection metrics.

HAROOD: Human Activity Classification and Out-of-Distribution Detection with Short-Range FMCW Radar