  Human 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 ({\mu}-D) signatures. In this paper, a MIMO radar is
used to formulate a novel micro-motion spectrogram for the angular velocity
({\mu}-{\omega}) in non-tangential scenarios. Combining both the {\mu}-D and
the {\mu}-{\omega} 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.
