It is difficult for the traditional Sparse Representation Classification (SRC) algorithm to enhance the performance of human activity recognition because of ignoring the correlation structure information hidden in sparse coefficient vectors of the test sample. To address this problem, a block sparse model-based human activity recognition approach was proposed. The human activity recognition problem was considered as a sparse representation-based classification problem on the basis of the inherent sparse block structure in human activity pattern. The block sparse Bayesian learning algorithm was used to solve the optimal sparse representation coefficients of a test sample for a linear combination of the training samples from the same class, and then the reconstruction residual of sparse coefficients was defined to determine the class of the test sample, which effectively improved the recognition rate of human activity pattern. The USC-HAD database containing different styles of human daily activity was selected to evaluate the effectiveness of the proposed approach. The experimental results show that the activity recognition rate of the proposed approach reaches 97.86%, which is increasd by 5% compared to the traditional human activity methods. These results demonstrate that the proposed method can effectively capture the discriminative information of the different activity pattern, and significantly improve the accuracy of human activity recognition.

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.