Aziz, FadyFadyAzizMetwally, OmarOmarMetwallyWeller, PascalPascalWellerSchneider, UrsUrsSchneiderHuber, MarcoMarcoHuber2023-06-212023-06-212022https://publica.fraunhofer.de/handle/publica/44321510.1109/RadarConf2248738.2022.97642022-s2.0-85143648802Human activity recognition is seen of great importance in the medical and surveillance fields. Radar has shown great feasibility for this field based on the captured micro-Doppler (μ-D) signatures. In this paper, a MIMO radar is used to formulate a novel micro-motion spectrogram for the angular velocity (μ -ω) in non-tangential scenarios. Combining both the μ-D and the μ -ω signatures have shown better performance. Classification accuracy of 88.9 % was achieved based on a metric learning approach. The experimental setup was designed to capture micro-motion signatures on different aspect angles and line of sight (LOS). The utilized training dataset was of smaller size compared to the state-of-the-art techniques, where eight activities were captured. A few-shot learning approach is used to adapt the pre-trained model for fall detection. The final model has shown a classification accuracy of 86.42 % for ten activities.enactivity recognitionmetric learningMicro-angular velocitymicro-DopplerMIMO radarconference paper