基于K-SVD的最大似然稀疏表示体域网动作分类算法

为有效提高体域网动作分类性能，本文提出了一种基于K-SVD的最大似然稀疏表示体域网动作分类算法. 该算法首先基于K-SVD优化学习算法，将不同动作模式训练样本按其所属类别分组优化训练，避免各类样本数据训练时相互干扰，得到不同动作模式类别所属的子字典，然后将其拼合构成一个完整字典，准确稀疏表示测试样本，最后基于最大似然稀疏模型准确估计稀疏表示系数残差，并得到测试样本所属类别. 实验结果表明，本文所提算法能够获得最优字典，基于最大似然稀疏表示可准确估计测试动作样本稀疏表示残差. 所提算法识别率明显优于传统稀疏表示动作分类算法，可有效提高体域网动作模式分类性能.

Maximum Likelihood Sparse Representation Activity Recognition Algorithm Based on K-SVD in Body Sensor Networks

In order to effectively improve the activity classification efficiency in body sensor networks, a maximum likelihood sparse representation algorithm based on K-SVD is proposed in this study. Firstly, all of activity pattern training samples are grouped according their classes to be trained, respectively. The mutual interference among different groups in the process of training can be avoided and sub-dictionaries for every class can be obtained. Then, these sub-dictionaries are used to construct an over-complete dictionary. And the dictionary is able to sparsely represent the testing samples precisely. The sparse representation coefficients are precisely approximated by maximum likelihood sparse model and the recognition result of testing samples are determined by the coefficients. The experimental results show that the proposed algorithm is able to obtain the optimal dictionary and the method based on maximum sparse representation can precisely estimate the representation error of testing activity samples. The accuracy of the proposed algorithm is obviously better than some conventional sparse-representation-based activity recognition algorithms. The proposed algorithm is able to effectively improve the activity pattern classification efficiency in body sensor networks.