HAROOD: Human Activity Classification and Out-of-Distribution Detection
  with Short-Range FMCW Radar
Sabri Mustafa Kahya Muhammet Sami Yavuz Eckehard Steinbach
We propose HAROOD as a short-range FMCW radar-based human activity classifier
and out-of-distribution (OOD) detector. It aims to classify human sitting,
standing, and walking activities and to detect any other moving or stationary
object as OOD. We introduce a two-stage network. The first stage is trained
with a novel loss function that includes intermediate reconstruction loss,
intermediate contrastive loss, and triplet loss. The second stage uses the
first stage's output as its input and is trained with cross-entropy loss. It
creates a simple classifier that performs the activity classification. On our
dataset collected by 60 GHz short-range FMCW radar, we achieve an average
classification accuracy of 96.51%. Also, we achieve an average AUROC of 95.04%
as an OOD detector. Additionally, our extensive evaluations demonstrate the
superiority of HAROOD over the state-of-the-art OOD detection methods in terms
of standard OOD detection metrics.