IEEE J Biomed Health Inform. 2023 May;27(5):2276-2285. doi: 10.1109/JBHI.2022.3186152. Epub 2023 May 4.
Respiration rate is an important healthcare indicator, and it has become a popular research topic in remote healthcare applications with Internet of Things. Existing respiration monitoring systems have limitations in terms of convenience, comfort, and privacy, etc. This paper presents a contactless and real-time respiration monitoring system, the so-called Wi-Breath, based on off-the-shelf WiFi devices. The system monitors respiration with both the amplitude and phase difference of the WiFi channel state information (CSI), which is sensitive to human body micro movement. The phase information of the CSI signal is considered and both the amplitude and phase difference are used. For better respiration detection accuracy, a signal selection method is proposed to select an appropriate signal from the amplitude and phase difference based on a support vector machine (SVM) algorithm. Experimental results demonstrate that the Wi-Breath achieves an accuracy of 91.2% for respiration detection, and has a 17.0% reduction in average error in comparison with state-of-the-art counterparts.
呼吸率是一个重要的健康指标,它已成为物联网远程医疗应用中的一个热门研究课题。现有的呼吸监测系统在便利性、舒适性和隐私性等方面存在局限性。本文提出了一种基于现成的 WiFi 设备的非接触式实时呼吸监测系统,即 Wi-Breath。该系统利用 WiFi 信道状态信息(CSI)的幅度和相位差来监测呼吸,这对人体微运动很敏感。CSI 信号的相位信息被考虑在内,同时使用幅度和相位差。为了提高呼吸检测的准确性,提出了一种信号选择方法,该方法基于支持向量机(SVM)算法,从幅度和相位差中选择合适的信号。实验结果表明,Wi-Breath 的呼吸检测准确率达到 91.2%,与最先进的同类产品相比,平均误差降低了 17.0%。