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Citation |
Cai F, Wu T, Lure FYM. Sensors (Basel) 2024; 24(14). |
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Copyright |
(Copyright © 2024, MDPI: Multidisciplinary Digital Publishing Institute) |
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DOI |
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PMID |
39066018 |
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PMCID |
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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. Language: en |
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Keywords |
Humans; Algorithms; deep learning; Gait/physiology; *Deep Learning; *Radar; Alzheimer Disease/diagnosis; contrastive learning; Hemodynamics/physiology; radar sensing; spectrogram; sub-band; Vital Signs/physiology |