Real-time detection of apnea via signal processing of time-series properties of RFID-based smart garments
Real-time detection of apnea via signal processing of time-series properties of RFID-based smart garments
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DOI：
10.1109/spmb.2016.7846871
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发表时间：
2016-12
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影响因子：
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通讯作者：
W. Mongan;I. Rasheed;K. Ved;Ariana Levitt;E. Anday;K. Dandekar;G. Dion;T. Kurzweg;A. Fontecchio
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文献类型：
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作者：
W. Mongan;I. Rasheed;K. Ved;Ariana Levitt;E. Anday;K. Dandekar;G. Dion;T. Kurzweg;A. Fontecchio
射频识别（RFID）标签的时间序列特性的信号处理以及用于服装设备的纺织针织天线的新工作使得通过不引人注目的无线可穿戴设备实时检测基于运动的工件成为可能。捕获接收信号强度指标（RSSI）作为时间序列信号，我们对受试者是否呼吸进行分类，估计受试者呼吸的速率，并对标签是否以线性、非拉伸的方式移动进行分类。通过消除使用呼吸和非呼吸样本数据训练分类器的需要（这在生物学上是不可实现的），我们改进了以前从RSSI信号中分类主体状态的努力。为了测试我们的方法，我们使用了一个可编程的呼吸婴儿模型，在5秒内准确检测呼吸活动的停止，在计算呼吸速率时，最大均方根误差为每分钟7。
Signal processing of time-series properties of Radio Frequency Identification (RFID) tags and novel work in textile knitted antennas for garment devices have enabled real-time detection of motion-based artifacts through unobtrusive, wireless, wearable devices. Capturing the Received Signal Strength Indicator (RSSI) as a time-series signal, we classify whether the subject is breathing or not, estimate the rate at which the subject is breathing, and classify whether the tag is moving in a linear, non-stretched fashion. We improve upon previous efforts to classify subject state from RSSI signals by eliminating the need to train the classifier with both breathing and non-breathing sample data (which is biologically infeasible). To test our approach, we use a programmable breathing infant mannequin yielding accurate detection of cessation of respiratory activity within 5 seconds, and a maximum root-mean-square error of 7 per minute when computing the respiratory rate.