O'Neill P, Mongan W M, Ross R, Acharya S, Fontecchio A, Dandekar K R
College of Computing and Informatics: Drexel University, Philadelphia, PA USA.
College of Engineering: Drexel University, Philadelphia, PA USA.
IEEE Signal Process Med Biol Symp. 2019 Dec;2019. doi: 10.1109/spmb47826.2019.9037861. Epub 2020 Mar 19.
With the use of a wireless, wearable, passive knitted smart fabric device as a strain gauge sensor, the proposed algorithm can estimate biomedical feedback such as respiratory activity. Variations in physical properties of Radio Frequency Identification (RFID) signals can be used to wirelessly detect physiological processes and states. However, it is typical for ambient noise artifacts to appear in the RFID signal making it difficult to identify physiological processes. This paper introduces a new technique for finding these repetitive physiological signals and identifying them into two states, and , using k-means clustering. The algorithm detects these biomedical events without the need to completely remove the noise components using a semi-unsupervised approach, and with these results, predict the next biomedical event using these classification results. This approach enables real-time noninvasive monitoring for use with actuating medical devices for therapy. Using this approach, the algorithm predicts the onset of respiratory activity in a simulated environment within approximately one second.
通过使用无线、可穿戴的无源针织智能织物设备作为应变计传感器，所提出的算法可以估计诸如呼吸活动等生物医学反馈。射频识别（RFID）信号物理特性的变化可用于无线检测生理过程和状态。然而，RFID信号中出现环境噪声伪影是很常见的，这使得识别生理过程变得困难。本文介绍了一种新技术，使用k均值聚类来找到这些重复的生理信号并将其识别为两种状态，即 和 。该算法使用半无监督方法检测这些生物医学事件，而无需完全去除噪声成分，并根据这些结果，利用这些分类结果预测下一个生物医学事件。这种方法能够实现实时无创监测，以用于驱动医疗设备进行治疗。使用这种方法，该算法在模拟环境中大约一秒内就能预测呼吸活动的开始。
