Affiliation:
1. James Watt School of Engineering University of Glasgow Glasgow UK
2. Shenzhen International Graduate School Tsinghua University Beijing China
3. Shanghai Jiaotong University Shanghai China
4. Department of Engineering University of Napoli “Parthenope” Napoli Italy
Abstract
AbstractRadar‐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.
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering
Cited by
1 articles.
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