Compressed acquisition and denoising recovery of EMGdi signal in WSNs and IoT

Fei Yun Wu, Kunde Yang, Zhi Yang

科研成果: 期刊稿件文章同行评审

31 引用 (Scopus)

摘要

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 l0 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.

源语言英语
页(从-至)2210-2219
页数10
期刊IEEE Transactions on Industrial Informatics
14
5
DOI
出版状态已出版 - 5月 2018

指纹

探究 'Compressed acquisition and denoising recovery of EMGdi signal in WSNs and IoT' 的科研主题。它们共同构成独一无二的指纹。

引用此