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ZHOU Fang, LU Zhi, ZHANG Ganlin, et al. A progressive EEG-supervised method for contactless sleep monitoring[J]. Journal of Radars, in press. doi: 10.12000/JR26120
Citation: ZHOU Fang, LU Zhi, ZHANG Ganlin, et al. A progressive EEG-supervised method for contactless sleep monitoring[J]. Journal of Radars, in press. doi: 10.12000/JR26120
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A Progressive EEG-supervised Method for Contactless Sleep Monitoring

DOI: 10.12000/JR26120 CSTR: 32380.14.JR26120

  • School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China

Funds:  Space Medical Experiment Project of CMSP (HYZHXMX01005)
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  • Corresponding author: CHEN Yan, eecyan@ustc.edu.cn
  • Received Date: 2026-06-30
  • Rev Recd Date: 2026-10-25
    • Available Online: 2026-10-30
    Abstract
  • Sleep monitoring is essential for assessing individual health. However, conventional polysomnography relies on multiple contact sensors and complex data acquisition procedures, making it difficult to meet the requirements of long-term, home-based monitoring. Radar sensing enables the contactless acquisition of respiratory and body movement signals during sleep, making it suitable for long-term home-based monitoring. Existing studies typically use large-scale public sleep datasets for pretraining or develop models for sleep staging and respiratory disorder detection based on respiratory features extracted from radar signals. The former requires cross-domain pretraining, resulting in relatively high model development and training costs, whereas the latter does not fully exploit EEG-derived knowledge, which is more directly associated with sleep-state assessment, thereby limiting sleep staging performance. To address these limitations, this paper proposes a progressive EEG-supervised method for contactless sleep monitoring. The proposed method leverages EEG and respiratory signals from a public dataset to progressively learn transferable discriminative representations through knowledge distillation and pretraining, which are then adapted to radar-based respiratory representations. This approach alleviates the modality gap between EEG and radar signals, thereby improving contactless sleep monitoring performance. Experimental results show that the proposed method effectively leverages prior knowledge from EEG to enhance radar-based sleep representation learning, achieving accuracies of 82.6% for sleep staging and 82.5% for respiratory disorder detection. These results provide a new technical perspective on contactless sleep monitoring.

     

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