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.

中文翻译：

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基于雷达的人体活动识别，使用去噪技术提高分类准确性

与摄像头和可穿戴设备等替代技术相比，基于雷达的人体活动识别被认为是老年人护理健康监测问题的一种有竞争力的解决方案。然而，原始雷达信号经常受到噪声、杂波和其他伪影的污染，这些伪影会严重影响识别性能，这凸显了增强雷达数据质量和提高分类模型准确性的前置技术的重要性。在这项研究中，提出了两种结合预处理技术的不同人类活动分类模型。作者将小波去噪方法引入基于循环平稳性的分类模型中，从而使分类精度得到大幅提高。为了解决传统预处理技术的局限性，提出了一种称为双相级联去噪和分类网络（DPDCNet）的深度神经网络模型，它执行端到端信号级分类并实现了最先进的准确性。所提出的模型显着减少了错误检测，并将利用雷达信号对老年人进行稳健的活动监测，从而使系统更接近实际部署实施。