Radar-based human activity recognition using denoising techniques to enhance classification accuracy

Yu, R., Du, Y., Li, J., Napolitano, A. and Le Kernec, J. (2024) Radar-based human activity recognition using denoising techniques to enhance classification accuracy. IET Radar, Sonar and Navigation, 18(2), pp. 277-293. (doi: 10.1049/rsn2.12501)

Abstract

Radar-based human activity recognition is considered as a competitive solution for the elderly care health monitoring problem, compared to alternative techniques such as cameras and wearable devices. However, raw radar signals are often contaminated with noise, clutter, and other artifacts that significantly impact recognition performance, which highlights the importance of prepossessing techniques that enhance radar data quality and improve classification model accuracy. In this study, two different human activity classification models incorporated with pre-processing techniques have been proposed. The authors introduce wavelet denoising methods into a cyclostationarity-based classification model, resulting in a substantial improvement in classification accuracy. To address the limitations of conventional pre-processing techniques, a deep neural network model called Double Phase Cascaded Denoising and Classification Network (DPDCNet) is proposed, which performs end-to-end signal-level classification and achieves state-of-the-art accuracy. The proposed models significantly reduce false detections and would enable robust activity monitoring for older individuals with radar signals, thereby bringing the system closer to a practical implementation for deployment.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Napolitano, Professor Antonio and Le Kernec, Dr Julien

Creator Roles

Le Kernec, J.: Conceptualization, Methodology, Project administration, Supervision, Writing – review and editing
Napolitano, A.: Conceptualization, Methodology, Resources, Software, Validation

• Authors: Yu, R., Du, Y., Li, J., Napolitano, A., and Le Kernec, J.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IET Radar, Sonar and Navigation
• Publisher: Wiley for the Institution of Engineering and Technology
• ISSN: 1751-8784
• ISSN (Online): 1751-8792
• Published Online: 08 November 2023
• Copyright Holders: Copyright: © 2023 The Authors
• First Published: First published in IET Radar, Sonar and Navigation 18(2): 277-293
• Publisher Policy: Reproduced under a Creative Commons licence
• Data DOI: 10.5525/gla.researchdata.848