# A MIMO Radar-Based Metric Learning Approach for Activity Recognition

arXiv: https://arxiv.org/abs/2111.01939 · HTML: https://mlsanity.com/paper/2111.01939/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 2 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks](https://mlsanity.com/paper/1711.02254/) (2017)
- [Doppler Radar for the Extraction of Biomechanical Parameters in Gait Analysis](https://mlsanity.com/paper/2005.05280/) (2020)
- [A MIMO Radar-based Few-Shot Learning Approach for Human-ID](https://mlsanity.com/paper/2110.08595/) (2021)

## Abstract (arXiv)

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 (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.