Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (5): 1492-1498.DOI: 10.11772/j.issn.1001-9081.2015.05.1492

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Block sparse Bayesian learning algorithm for reconstruction and recognition of gait pattern from wireless body area networks

WU Jianning, XU Haidong   

  1. College of Mathematics and Computer Science, Fujian Normal University, Fuzhou Fujian 350007, China
  • Received:2014-12-19 Revised:2015-01-23 Online:2015-05-10 Published:2015-05-14


吴建宁, 徐海东   

  1. 福建师范大学 数学与计算机科学学院, 福州 350007
  • 通讯作者: 吴建宁
  • 作者简介:吴建宁(1969-),男,福建福州人,副教授,博士,主要研究方向:生物医学信号处理、无线人体传感网与医学应用; 徐海东(1991-),男,福建莆田人,硕士研究生,主要研究方向:无线传感网.
  • 基金资助:



In order to achieve the optimal performance of gait pattern recognition and reconstruction of non-sparse acceleration data from Wireless Body Area Networks (WBANs)-based telemonitoring, a novel approach to apply the Block Sparse Bayesian Learning (BSBL) algorithm for improving the reconstruction performance of non-sparse accelerometer data was proposed, which contributes to achieve the superior performance of gain pattern recognition. Its basic idea is that, in view of the gait pattern and Compressed Sensing (CS) framework of WBAN-based telemonitoring, the original acceleration-based data acquired at sensor node in WBAN was compressed only by spare measurement matrix (the simple linear projection algorithm), and the compressed data was transmitted to the remote terminal, where BSBL algorithm was used to perfectly recover the non-sparse acceleration data that assumed as block structure by exploiting intra-block correlation for further gait pattern recognition with high accuracy. The acceleration data from the open USC-HAD database including walking, running, jumping, upstairs and downstairs activities were employed for testing the effectiveness of the proposed method. The experiment results show that with acceleration-based data, the reconstruction performance of the proposed BSBL algorithm can significantly outperform some conventional CS algorithms for sparse data, and the best accuracy of 98% can be obtained by BSBL-based Support Vector Machine (SVM) classifier for gait pattern recognition. These results demonstrate that the proposed method not only can significantly improve the reconstruction performance of non-sparse acceleration data for further gait pattern recognition with high accuracy but also is very helpful for the design of low-cost sensor node hardware with lower energy consumption, which will be a potential approach for the energy-efficient WBAN-based telemonitoring of human gait pattern in further application.

Key words: Block Sparse Bayesian Learning (BSBL) algorithm, Compressed Sensing (CS), Wireless Body Area Network (WBAN), gait pattern recognition



关键词: 块稀疏贝叶斯学习算法, 压缩感知, 体域网, 步态模式识别

CLC Number: