PDF(Pubmed)
Abstract：
Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time-frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper proposes an Enhanced Band-Dependent Learning framework (E-BDL) comprising an adaptive sub-band filtering module, a representation learning module, and a sub-view contrastive module to fully detect band-dependent features in sub-frequency bands and leverage them for classification. Experimental validation is conducted on two radar datasets, including gait abnormality recognition for Alzheimer\'s disease (AD) and AD-related dementia (ADRD) risk evaluation and vital-sign monitoring for hemodynamics scenario classification. For hemodynamics scenario classification, E-BDL-ResNet achieves competitive performance in overall accuracy and class-wise evaluations compared to recent methods. For ADRD risk evaluation, the results demonstrate E-BDL-ResNet\'s superior performance across all candidate models, highlighting its potential as a clinical tool. E-BDL effectively detects salient sub-bands in TFRs, enhancing representation learning and improving the performance and interpretability of DL-based models.
摘要：
雷达传感器,利用多普勒效应，使动态和生理运动的非侵入性捕获，同时保护隐私。深度学习(DL)促进了用于医疗应用(如步态识别和生命体征测量)的雷达感测。然而,带相关模式，指示与时频表示(TFR)中的频率相关的模式和功率标度的变化，挑战使用DL的雷达传感应用。在表示学习过程中，可能会忽略频率相关的特性和具有较低功率标度的特性。本文提出了一种增强的带相关学习框架（E-BDL），包括一个自适应子带滤波模块，表示学习模块，子视图对比模块，以完全检测子频带中的频带相关特征，并利用它们进行分类。在两个雷达数据集上进行了实验验证，包括用于阿尔茨海默病（AD）和AD相关性痴呆（ADRD）风险评估的步态异常识别以及用于血流动力学情景分类的生命体征监测。对于血液动力学情景分类，与最近的方法相比，E-BDL-ResNet在总体准确性和分类评估方面具有竞争力。对于ADRD风险评估，结果表明，E-BDL-ResNet在所有候选模型中的卓越性能，强调其作为临床工具的潜力。E-BDL有效地检测TFR中的显著子带，增强表示学习，提高基于DL的模型的性能和可解释性。
