用块稀疏贝叶斯学习算法重构识别体域网步态模式

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

针对低功耗体域网步态远程监测终端非稀疏加速度数据重构和步态模式识别性能优化问题,提出了一种基于块稀疏贝叶斯学习的体域网远程步态模式重构识别新方法,该方法基于体域网远程步态监测系统架构和压缩感知框架,在体域网传感节点利用线性稀疏矩阵压缩原始加速度数据,减少传输数据量,降低其功耗,同时在远程终端基于块稀疏贝叶斯学习算法充分利用加速度数据块结构内在相关性,获取加速度数据内在稀疏性,有效提高非稀疏加速度数据重构性能,为准确识别步态模式提供可靠的数据支撑。采用USC-HAD数据库中行走、跑、跳、上楼、下楼五种步态运动的加速度数据验证新方法的有效性,实验结果表明,基于所提算法的加速度数据重构性能明显优于传统压缩感知重构算法性能,使基于支持向量机多步态分类器识别准确率可达98%,显著提高体域网远程步态模式识别性能。所提新方法不仅有效提高非稀疏加速度数据重构和步态模式识别性能,并且也有助于设计低功耗、低成本的体域网加速度数据采集系统,为体域网远程监测步态模式变化提供一个新方法和新思路。

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 nonsparse 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.