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Compressed Acquisition and Denoising Recovery of EMGdi Signal in WSNs and IoT
IEEE Transactions on Industrial Informatics ( IF 12.3 ) Pub Date : 2018-05-01 , DOI: 10.1109/tii.2017.2759185
Fei-Yun Wu , Kunde Yang , Zhi Yang

Telemonitoring of diaphragmatic electromyogram (EMGdi) signal in wireless sensor networks (WSNs) and Internet of Things (IoT) holds the promise to be an evolving direction in personalized medicine. The WSNs and IoT enable EMGdi information telemonitoring and communications technologies play important roles in the process of personal medical care, especially for the respiratory diseases. However, while designing such a system, one should consider the required functionality, miniaturization, energy efficiency, etc., to make fewer resources required in WSNs and IoT. Conventional methods of data acquisition cannot energy-effectively compress data with reduced device costs. Different from the traditional compression methods, compressed sensing (CS) takes promising steps toward these challenges. Unfortunately, EMGdi is not sparse in time domain. Hence, current CS algorithms are extremely difficult to use directly for recovering EMGdi. In order to satisfy the requirements of applications of personal medical care in WSNs and IoT, this study proposes an approximated $l_0$ norm based method to search the solution via the gradient descent method, then projects the searched solution to the reconstruction feasible set. Meanwhile, this study adopts a new wavelet threshold based method to denoise the electrocardiographic interference. Experimental results are provided to testify the performance of the proposed methods.

中文翻译:

WSN和IoT中EMGdi信号的压缩采集和降噪恢复

无线传感器网络(WSN)和物联网(IoT)中的diaphragm肌肌电图(EMGdi)信号的远程监控有望成为个性化医疗领域不断发展的方向。WSN和IoT使EMGdi信息远程监控和通信技术在个人医疗保健过程中发挥着重要作用,尤其是在呼吸系统疾病方面。但是,在设计这样的系统时,应该考虑所需的功能,小型化,能效等,以减少WSN和IoT中所需的资源。常规的数据采集方法无法以降低的设备成本有效地压缩数据。与传统的压缩方法不同,压缩感测(CS)为应对这些挑战采取了有希望的步骤。不幸的是,EMGdi在时域上并不稀疏。因此,当前的CS算法很难直接用于恢复EMGdi。为了满足个人医疗保健在WSN和IoT中的应用需求,本研究提出了一种基于近似$ l_0 $范数的方法,通过梯度下降法搜索解决方案,然后将搜索到的解决方案投影到重建可行集上。同时,本研究采用了一种基于小波阈值的新方法来消除心电图干扰。提供实验结果以证明所提出方法的性能。这项研究提出了一种基于范本的近似方法,通过梯度下降法搜索解,然后将搜索到的解投影到重建可行集上。同时,本研究采用了一种基于小波阈值的新方法来消除心电图干扰。提供实验结果以证明所提出方法的性能。这项研究提出了一种基于范本的近似方法,通过梯度下降法搜索解,然后将搜索到的解投影到重建可行集上。同时,本研究采用了一种基于小波阈值的新方法来消除心电图干扰。提供实验结果以证明所提出方法的性能。
更新日期:2018-05-01
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