面向可穿戴生理信号的压缩感知实时重构 |
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引用本文: | 程云飞,叶娅兰,侯孟书,何文文,李云霞.面向可穿戴生理信号的压缩感知实时重构[J].电子科技大学学报(自然科学版),2021,50(1):36-42. |
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作者姓名: | 程云飞 叶娅兰 侯孟书 何文文 李云霞 |
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作者单位: | 电子科技大学计算机科学与工程学院 成都 611731;电子科技大学自动化工程学院 成都 611731 |
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基金项目: | 国家自然科学基金(61976047);四川省科技厅重点研发项目(2019YFG0122, 2020YFG0087, 2020YFG0326) |
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摘 要: | 传统的迭代式压缩感知重构算法由于计算复杂度高,数据处理实时性差,难以在实际的可穿戴设备中发挥作用.该文结合深度学习中的一维扩张卷积和残差网络,提出了一种适用于可穿戴健康监护的非迭代式压缩感知实时重构算法.该方法基于大量生理信号数据训练一个用于压缩感知重构的网络模型,该模型可以对生理信号进行快速精确重构.通过在两个公开的...
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关 键 词: | 压缩感知 深度学习 非迭代方法 生理信号 可穿戴设备 |
收稿时间: | 2020-07-01 |
Real-Time Compressed Sensing Reconstruction for Wearable Physiological Signals |
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Affiliation: | 1.School of Computer Science and Engineering, University of Electronic Science and Technology of China Chengdu 6117312.School of Automation Engineering, University of Electronic Science and Technology of China Chengdu 611731 |
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Abstract: | 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. |
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