Reliable and contactless measurements of vital signs, such as respiration and heart rate, are still unmet needs in clinical and home settings. Mm-wave radar and video-based technologies are promising, but currently, the signal processing-based vital sign extraction methods are prone to body motion disruptions or illumination variations in the surrounding environment. Here we propose an image segmentation-based method to extract vital signs from the recorded video and mm-wave radar signals. The proposed method analyses time–frequency spectrograms obtained from Short-Time Fourier Transform rather than individual time-domain signals. This leads to much-improved robustness and accuracy of the heart rate and respiration rate extraction over existing methods. The experiments were conducted under pre- and post-exercise conditions and were repeated on multiple individuals. The results are evaluated by using four metrics against the gold standard contact-based measurements. Significant improvements were observed in terms of precision, accuracy, and stability. The performance was reflected by achieving an averaged Pearson correlation coefficient (PCC) of 93.8% on multiple subjects. We believe that the proposed estimation method will help address the needs for the increasingly popular remote cardiovascular sensing and diagnosing posed by Covid-19.

对呼吸和心率等生命体征的可靠和非接触式测量在临床和家庭环境中仍然没有得到满足。毫米波雷达和基于视频的技术前景广阔，但目前基于信号处理的生命体征提取方法容易出现身体运动中断或周围环境的照明变化。在这里，我们提出了一种基于图像分割的方法，从记录的视频和毫米波雷达信号中提取生命体征。所提出的方法分析从短时傅里叶变换获得的时频谱图，而不是单个时域信号。与现有方法相比，这使得心率和呼吸率提取的鲁棒性和准确性大大提高。这些实验是在运动前和运动后条件下进行的，并对多个个体重复进行。通过使用四个指标来对照基于接触式测量的黄金标准来评估结果。在精度、准确性和稳定性方面观察到显着改进。多个科目的平均皮尔逊相关系数 (PCC) 达到 93.8%，反映了这一表现。我们相信，所提出的估计方法将有助于满足 Covid-19 带来的日益流行的远程心血管传感和诊断的需求。