Radar-based human activity recognition with adaptive thresholding towards resource constrained platforms

Li, Z., Le Kernec, J. , Abbasi, Q. , Fioranelli, F. , Yang, S. and Romain, O. (2023) Radar-based human activity recognition with adaptive thresholding towards resource constrained platforms. Scientific Reports, 13, 3473. (doi: 10.1038/s41598-023-30631-x)

Abstract

Radar systems are increasingly being employed in healthcare applications for human activity recognition due to their advantages in terms of privacy, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms are however often complex, focusing on a single domain of radar, and requiring significant computational resources that prevent their deployment in embedded platforms which often have limited memory and computational resources. To address this issue, we present an adaptive magnitude thresholding approach for highlighting the region of interest in the multi-domain micro-Doppler signatures. The region of interest is beneficial to extract salient features, meanwhile it ensures the simplicity of calculations with less computational cost. The results for the proposed approach show an accuracy of up to 93.1% for six activities, outperforming state-of-the-art deep learning methods on the same dataset with an over tenfold reduction in both training time and memory footprint, and a twofold reduction in inference time compared to a series of deep learning implementations. These results can help bridge the gap toward embedded platform deployment.

• Item Type: Articles
• Additional Information: The authors are grateful to Professor Muhammad Imran, University of Glasgow supported by Engineering and Physical Sciences Research Council (EPSRC) grant EP/T021020/1. The authors acknowledge financial support, the British Council 515095884 and Campus France 44764WK (PHC Alliance France-UK).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Yang, Dr Shufan and Fioranelli, Dr Francesco and Romain, Professor Olivier and Abbasi, Dr Qammer and Le Kernec, Dr Julien and Li, Zhenghui
• Authors: Li, Z., Le Kernec, J., Abbasi, Q., Fioranelli, F., Yang, S., and Romain, O.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Scientific Reports
• Publisher: Nature Research
• ISSN: 2045-2322
• ISSN (Online): 2045-2322
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Scientific Reports 13: 3473
• Publisher Policy: Reproduced under a Creative Commons License
• Data DOI: 10.5525/gla.researchdata.848

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
307829		Quantum-Inspired Imaging for Remote Monitoring of Health & Disease in Community Healthcare	Jonathan Cooper	Engineering and Physical Sciences Research Council (EPSRC)	EP/T021020/1	ENG - Biomedical Engineering