TY - JOUR
T1 - Compressed acquisition and denoising recovery of EMGdi signal in WSNs and IoT
AU - Wu, Fei Yun
AU - Yang, Kunde
AU - Yang, Zhi
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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.
KW - Approximated l (AL0) norm
KW - compressed sensing (CS)
KW - diaphragmatic electromyogram (EMGdi)
KW - electrocardiographic (ECG)
KW - Internet of Things (IoT)
KW - wireless sensor networks (WSNs)
UR - http://www.scopus.com/inward/record.url?scp=85030788749&partnerID=8YFLogxK
U2 - 10.1109/TII.2017.2759185
DO - 10.1109/TII.2017.2759185
M3 - 文章
AN - SCOPUS:85030788749
SN - 1551-3203
VL - 14
SP - 2210
EP - 2219
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 5
ER -