Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras; however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs.

翻译：生命体征（呼吸和心跳）监测对于患者护理和睡眠疾病预防至关重要。当前大多数解决方案基于可穿戴传感器或摄像头，但前者可能影响睡眠质量，后者常引发隐私问题。为解决这些不足，我们提出Wital——一种基于低成本且广泛商用的现成（COTS）Wi-Fi设备的无接触体征监测系统。需要克服两大挑战：首先，呼吸/心跳引起的躯干形变十分微弱，如何有效捕捉此类形变？其次，翻身等动作会影响体征监测的准确性，如何避免此类不利影响？针对前者，我们提出一种非视距（NLOS）感知模型，利用莱斯K理论建模视距（LOS）与NLOS信号能量比与体征监测能力之间的关系，并以此模型指导系统构建，以更好地捕捉呼吸/心跳引起的形变。针对后者，我们提出一种基于运动规律检测的动作分割方法，能准确区分呼吸与其他运动，并剔除包含翻身等动作的时间段以消除不利影响。我们在低成本COTS设备上实现并验证了Wital。实验结果表明了Wital在体征监测方面的有效性。