airBP: Monitor Your Blood Pressure with Millimeter-Wave in the Air

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Internet of Things Pub Date : 2023-08-09 DOI:10.1145/3614439
Yumeng Liang, Anfu Zhou, Xinzhe Wen, Wei Huang, Pu Shi, Lingyu Pu, Huanhuan Zhang, Huadong Ma
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Abstract

Blood pressure (BP), an important vital sign to assess human health, is expected to be monitored conveniently. The existing BP monitoring methods, either traditional cuff-based or newly-emerging wearable-based, all require skin contact, which may cause unpleasant user experience and is even injurious to certain users. In this paper, we explore contact-less BP monitoring and propose airBP, which emits millimeter-wave signals toward a user’s wrist, and captures the reflected signal bounded off from the pulsating artery underlying the wrist. By analyzing the reflected signal strength of the signal, airBP generates arterial pulse and further estimates BP by exploiting the relationship between the arterial pulse and BP. To realize airBP, we design a new beam-forming method to keep focusing on the tiny and hidden wrist artery, by leveraging the inherent periodicity of the arterial pulse. Moreover, we custom-design a pre-training and neural network architecture, to combat the challenges from the arterial pulse sparsity and ambiguity, so as to estimate BP accurately. We prototype airBP using a coin-size COTS mmWave radar and perform extensive experiments on 41 subjects. The results demonstrate that airBP accurately estimates systolic and diastolic BP, with the mean error of -0.30 mmHg and -0.23 mmHg, as well as the standard deviation error of 4.80 mmHg and 3.79 mmHg (within the acceptable range regulated by the FDA’s AAMI protocol), respectively, at a distance up to 26 cm.
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airBP:用毫米波监测你的血压
血压(BP)是衡量人体健康的重要生命指标,有望实现便捷的监测。现有的血压监测方法,无论是传统的袖带式还是新兴的可穿戴式,都需要与皮肤接触,这可能会造成不愉快的用户体验,甚至对某些用户造成伤害。在本文中,我们探索了非接触式血压监测,并提出了airBP,它向用户的手腕发射毫米波信号,并捕获来自手腕下方脉动动脉的反射信号。通过分析信号的反射信号强度,airBP产生动脉脉搏,并利用动脉脉搏与血压之间的关系进一步估计血压。为了实现airBP,我们设计了一种新的波束形成方法,利用动脉脉冲固有的周期性来持续聚焦微小且隐藏的手腕动脉。此外,我们定制了一种预训练和神经网络架构,以克服动脉脉冲稀疏性和模糊性带来的挑战,从而准确地估计BP。我们使用硬币大小的COTS毫米波雷达对airBP进行原型设计,并对41名受试者进行了广泛的实验。结果表明,在长达26厘米的距离内,airBP准确地估计了收缩压和舒张压,平均误差分别为-0.30 mmHg和-0.23 mmHg,标准差误差分别为4.80 mmHg和3.79 mmHg(在FDA AAMI协议规定的可接受范围内)。
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CiteScore
5.20
自引率
3.70%
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0
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