用于低功耗的体域网加速度数据压缩感知设计

doi:10.3969/j.issn.0258-8021.2015. 06.006

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摘要为有效降低体域网加速度数据远程获取系统功耗，提出一种用于低功耗的体域网加速度数据压缩感知处理框架。首先基于优化的稀疏二进制测量矩阵，线性投影压缩体域网传感节点的加速度数据，降低相应节点压缩算法的复杂性和数据传输量；在此基础上，基于块稀疏贝叶斯学习模型，构建非稀疏加速度数据压缩感知重构算法，实现低功耗远程获取加速度数据。实验采用USC-HAD数据库的加速度数据，评估所提方法的有效性。结果表明，当优化稀疏二进制测量矩阵中每列非零元素个数为6、压缩率为50%时，能够获得与传统优化高斯随机矩阵、伯努利随机矩阵相同的压缩重构误差（约0.004 5）；与一些传统的压缩感知重构算法相比，所提出的重构算法提高信噪比约17 dB，显著改善重构性能，可有效实现低功耗远程获取加速度数据。该方法有助于传感节点硬件简化设计，改善加速度数据重构性能，为构建低功耗体域网人体活动远程监测系统提供新的思路和方法。

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	吴建宁^1*徐海东¹王珏²

关键词 ：压缩感知, 低功耗, 加速度数据, 体域网, 远程医疗

Abstract：This paper proposed a novel scheme of energy-efficient compressed sensing framework for processing the acceleration-based data acquired from wireless body area networks (WBANs), in order to save the energy of WBANs-based system. With the optimal scheme of sparse binary matrices, the raw accelerometer data with nonsparse is compressed by linearly projected at sensor node before their transmission, and then the compressed data is reconstructed by the novel block Bayesian learning algorithm (BSBL) at remote terminal. The acceleration data from USC-HAD dataset of Southern California was used to evaluate the effectiveness of our proposed technique. The experimental results showed that the optimal scheme of sparse binary matrix could obtain the same reconstruction error (0.0045) as Gaussian or Bernouilli random matrix when a number of nonzero values were selected as 6 in each column of the designed sparse binary matrix and the ratio of compression was 50%. Besides, compared with the traditional CS-based reconstruction algorithms, our proposed BSBL algorithm for reconstruction of acceleration data could increase by 17 dB of signalnoise ratio, significantly improving the performance of reconstruction of acceleration data. These results suggested that, with the optimal design of sparse binary matrix, the designed compressed sensing framework could acquired the acceleration data at sub-Nyquist sampling rate and greatly reduce the number of transmitted data by simple linear transform at sensor node for saving energy. It also can contribute to improving the performance of reconstruction of non-sparse acceleration data by using BSBL. Our work can provide a novel approach for further practical implementation such as the design of simple hardware of sensor node, improvement of the performance of reconstruction of accelerationdata and the development of WBANs\|based system with lower energy consumption for remote monitoring physical activity.

Key words： compressed sensing low energy consumption acceleration-based data wireless body area networks telemedicine

基金资助:国家科技支撑项目（2012BAI33B01）；福建省自然科学基金（2013J01220）；福建省高等学校教学改革研究专项（JAS14674）

引用本文:

吴建宁^1*徐海东¹王珏². 用于低功耗的体域网加速度数据压缩感知设计[J]. 中国生物医学工程学报, 2015, 34(6): 677-685.
Wu Jianning^1*Xu Haidong¹Wang Jue². A Novel Application of Compressed Sensing for the Accelerometer Data from Wireless Body Area Network with Low Energy Consumption. journal1, 2015, 34(6): 677-685.

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http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2015. 06.006 或 http://cjbme.csbme.org/CN/Y2015/V34/I6/677

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