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| A Fast Sparse Representation Classification Method for Human Activity-Recognition Based on Random Projection |
| College of Mathematics and Computer Science, Fujian Normal University, Fuzhou 350007, China |
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Abstract In this paper, a fast sparse representation classification method for human activity recognition based on random projection was proposed, in order to minimize the energy consumption and accurately recognize human activities from wireless body sensor networksbased telemonitoring system of human daily activity. The basic idea of the proposed method is that the random projection way of compressed sensing theory is used to reduce the amount of sampling on sensor nodes within body sensor network, and then the smaller number of nearest neighbor training samples within the neighbor classes of testing sample, which can optimally liner reconstruction testing sample, are obtained to construct the training sample set of the sparse representation of testing sample. Thus, a fast sparse representation classification algorithm with superior performance of generalization can be developed for capturing valuable features of human activity and improving the recognition rate on the basis of the lower energy consumption and computation complexity of algorithm. The multi-class activity data from international open wearable sensor action recognition database WRAD was selected to evaluate the effectiveness of our method. The experimental results showed that, when the data compression rate was 50%, the proposed algorithm could obtain the highest average recognition rate (92.78%), which was increased by approximately 5% compared with that of the traditional sparse representation classification algorithms. Meanwhile, the operating time of our proposed algorithm was significantly reduced compared with the above traditional methods.We believed that the proposed algorithm could not only effectively reduce the computational complexity and its running time but also significantly enhance the human activity recognition accuracy, providing a new idea and method for developing the fast sparse representation classification algorithm for activity recognition.
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