Real-Time Compressed Sensing Reconstruction for Wearable Physiological Signals

被引：0

作者：

Cheng Y.-F. ^{[1
]}

Ye Y.-L. ^{[1
]}

Hou M.-S. ^{[1
]}

He W.-W. ^{[1
]}

Li Y.-X. ^{[2
]}

机构：

[1] School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu

[2] School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu

来源：

Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2021年 / 50卷 / 01期

关键词：

Compressed sensing; Deep learning; Non-iterative method; Physiological signal; Wearable device;

D O I：

10.12178/1001-0548.2020268

中图分类号：

学科分类号：

摘要：

The traditional iterative compressed sensing reconstruction algorithm is difficult to play a role in actual wearable devices because of its high computational complexity and poor real-time data processing. In this paper, a non-iterative compressed sensing real-time reconstruction algorithm suitable for wearable health monitoring is proposed by combining one-dimensional dilated convolution and residual network in deep learning. The proposed method trains a network model for compressed sensing reconstruction based on a large number of physiological signal data, and the trained neural network model can accurately reconstruct physiological signals at a very fast speed. Experiments on 2 open physiological signal data sets show that the proposed method has higher reconstruction accuracy than the existing reconstruction algorithms based on deep learning. The proposed method can reconstruct a 2 s signal frame in only about 0.7 ms on the computer used in this paper. This is about 2~3 orders of magnitude faster than the traditional iterative compressed sensing reconstruction algorithm. Therefore, the method proposed in this paper has excellent real-time performance. Copyright ©2021 Journal of University of Electronic Science and Technology of China. All rights reserved.

引用

页码：36 / 42

页数：6

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