English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Human activity recognition is seen of great importance in the medical andsurveillance fields. Radar has shown great feasibility for this field based onthe captured micro-Doppler ({\mu}-D) signatures. In this paper, a MIMO radar isused to formulate a novel micro-motion spectrogram for the angular velocity({\mu}-{\omega}) in non-tangential scenarios. Combining both the {\mu}-D andthe {\mu}-{\omega} signatures have shown better performance. Classificationaccuracy of 88.9% was achieved based on a metric learning approach. Theexperimental setup was designed to capture micro-motion signatures on differentaspect angles and line of sight (LOS). The utilized training dataset was ofsmaller size compared to the state-of-the-art techniques, where eightactivities were captured. A few-shot learning approach is used to adapt thepre-trained model for fall detection. The final model has shown aclassification accuracy of 86.42% for ten activities.

A MIMO Radar-Based Metric Learning Approach for Activity Recognition