Non-Invasive RF Sensing for Detecting Breathing Abnormalities Using Software Defined Radios
Non-Invasive RF Sensing for Detecting Breathing Abnormalities Using Software Defined Radios
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DOI：
10.1109/jsen.2020.3035960
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发表时间：
2021-02-15
影响因子：
4.3
通讯作者：
Imran, Muhammad Ali
中科院分区：
文献类型：
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作者：
Ashleibta, Aboajeila Milad;Abbasi, Qammer H.;Imran, Muhammad Ali
包括呼吸检测和心率在内的生物标志物的非接触式连续监测是评估患者总体身体健康的基本生命体征。与需要专用设备(如可穿戴传感器)的现有方法相比，可以合成射频(RF)信号以在非接触式设置下连续监测呼吸频率。本文提出了一种基于通用软件无线电外设(USRP)平台的无穿戴传感器非接触式呼吸频率检测方法。我们的系统利用信道状态信息(CSI)来记录在多个子载波中通过正交频分多路复用(Ofdm)呼吸引起的微小运动。我们给出了我们的呼吸频率检测与可穿戴传感器(地面真相)对单个人类受试者的结果进行了比较。在本文中，我们使用无线数据来训练、验证和测试不同的机器学习(ML)算法，以根据呼吸频率将USRP数据分类为正常呼吸、浅呼吸和高呼吸。虽然使用K-近邻(KNN)、判别分析(DA)、朴素贝叶斯(NB)和决策树(DT)算法建立了不同的ML模型，但结果表明基于KNN的模型每次对我们的数据提供最高的准确率(91%)。DT(71.131%)、DA(59.72%)、NB(48.99%)。本文的结果表明，基于USRP的呼吸频率与可穿戴传感器相当，证明了我们的方法在初诊或急性情况下准确监测患者呼吸频率的潜在应用。
The non-contact continuous monitoring of biomarkers comprising breathing detection and heart rate are essential vital signs to evaluate the general physical health of a patient. As compared to existing methods that need dedicated equipment (such as wearable sensors), the radio frequency (RF) signals can be synthesised to continuously monitor breathing rate in a contact-less setting. In this paper, we proposed the contact less breathing rate detection using universal software radio peripheral (USRP) platform without any wearable sensor. Our system leverage on the channel state information (CSI) to record the minute movement caused by breathing over orthogonal frequency division multiplexing (OFDM) in multiple sub-carriers. We presented a comparison of our breathing rate detection with wearable sensor (ground truth) results for single human subject. In this paper, we used wireless data to train, validate and test different machine learning (ML) algorithms to classify USRP data into normal, shallow and elevated breathing depending on the breathing rate. Although different ML models were developed using the K-Nearest Neighbor (KNN), Discriminant Analysis (DA), Naive Bayes (NB) and Decision Tree (DT) algorithms, however results showed KNN based model provided the highest accuracy for our data (91%) each time the trial was made. DT (71.131%), DA (59.72%) and NB (48.99%). Results presented in this paper showed that USRP based breathing rate is comparable to the wearable sensor demonstrating the potential application of our method to accurately monitor breathing rate of patients in primary or acute setting.