Radar based human sensing especially vital signs(respiration/heartbeat) detection has attracted much attention. The basic principle of vital signs detection is to detect the tiny displacement caused by physiological movements, which are difficult to detect at long distance and are easily disturbed by human random body movements. To address this issue, we propose two strategies in this paper. In order not to lose the target signal, we select radar signals from multiple range bins within the neighborhood of target position as candidate signals, where the target position is obtained through examining radar signal variance. But the cost of candidate signals is to bring in noise signals that cannot be distinguished from physiological signals by using traditional signal processing methods. So a spatio-temporal dense network (ST-DenseNet) is proposed to extract physiological signals from candidate signals, which learns the most discriminative features to distinguish between physiological signals and noise signals through convolving spatial features at different temporal scales for strengthening feature fusion and using dense connections in the network for enhancing generalization capability. Based on these two strategies, our method realizes accurate vital signs detection over a large spatial range and achieves good robustness to disturbances such as body movements. Extensive experiments on wide space and multiple subjects confirms the superiority of our method. The error of respiration and heartbeat detection are reduced to 0.93bpm (beats per minute) and 3.83bpm when the sensing scope is improved to 2 meters.


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    Titel :

    Spatio-Temporal Dense Network for Vital Signs Detection Using FMCW Radar


    Beteiligte:
    Zhao, Qian (Autor:in) / Li, Hongchun (Autor:in) / Tian, Jun (Autor:in) / Xie, Lili (Autor:in) / Yoshioka, Takahiro (Autor:in) / Ide, Kenta (Autor:in) / Shiraishi, Masahiro (Autor:in) / Konno, Takeshi (Autor:in)


    Erscheinungsdatum :

    10.10.2023


    Format / Umfang :

    1551233 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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