Wu Jianning, Xu Haidong
School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, 350007, Fujian, China.
Biomed Eng Online. 2016 Mar 5;15:27. doi: 10.1186/s12938-016-0142-9.
The compressed sensing (CS) of acceleration data has been drawing increasing attention in gait telemonitoring application. In such application, there still exist some challenging issues including high energy consumption of body-worn device for acceleration data acquisition and the poor reconstruction performance due to nonsparsity of acceleration data. Thus, the novel scheme of compressive sensing of acceleration data is needed urgently for solutions that are found to these issues.
In our scheme, the sparse binary matrix is firstly designed as an optimal measurement matrix only containing a smallest number of nonzero entries. And then the block sparse Bayesian learning (BSBL) algorithm is introduced to reconstruct acceleration data with high fidelity by exploiting block sparsity. Finally, some commonly used gait classification models such as multilayer perceptron (MLP), support vector machine (SVM) and KStar are applied to further validate the feasibility of our scheme for gait telemonitoring application.
The acceleration data were selected from open Human Activity Dataset of Southern California University (USC-HAD). The optimal sparse binary matrix (a smallest number of nonzero entries is 8) is as strong as the full optimal measurement matrix such as Gaussian random matrix. Moreover, BSBL algorithm significantly outperforms existing conventional CS reconstruction algorithms, and reaches the maximal signal-to-noise ratio value (70 dB). In comparison, MLP is best for gait classification, and it can classify upstairs and downstairs patterns with best accuracy of 95 % and seven gait patterns with maximal accuracy of 92 %, respectively.
These results show that sparse binary matrix and BSBL algorithm are feasibly applied in compressive sensing of acceleration data to achieve the perfect compression and reconstruction performance, which has a great potential for gait telemonitoring application.
加速度数据的压缩感知（CS）在步态远程监测应用中受到越来越多的关注。在这种应用中，仍然存在一些具有挑战性的问题，包括用于加速度数据采集的可穿戴设备能耗高，以及由于加速度数据的非稀疏性导致的重建性能较差。因此，迫切需要一种新颖的加速度数据压缩感知方案来解决这些问题。
在我们的方案中，首先将稀疏二元矩阵设计为仅包含最少数量非零元素的最优测量矩阵。然后引入块稀疏贝叶斯学习（BSBL）算法，通过利用块稀疏性来高保真地重建加速度数据。最后，应用一些常用的步态分类模型，如多层感知器（MLP）、支持向量机（SVM）和KStar，进一步验证我们的方案在步态远程监测应用中的可行性。
加速度数据选自南加州大学开放的人类活动数据集（USC-HAD）。最优稀疏二元矩阵（最少数量的非零元素为8）与诸如高斯随机矩阵等全最优测量矩阵一样强大。此外，BSBL算法明显优于现有的传统CS重建算法，并达到最大信噪比（70 dB）。相比之下，MLP在步态分类方面表现最佳，它可以分别以95%的最佳准确率对上下楼梯模式进行分类，以92%的最大准确率对七种步态模式进行分类。
这些结果表明，稀疏二元矩阵和BSBL算法可有效地应用于加速度数据的压缩感知，以实现完美的压缩和重建性能，在步态远程监测应用中具有巨大潜力。
