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| A Novel Application of Compressed Sensing for the Accelerometer Data from Wireless Body Area Network with Low Energy Consumption |
1 School of Mathematics and Computer Science of Fujian Normal University, Fuzhou 350007, China
2 Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi’an Jiaotong University, Xi’an 710049, China
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Abstract This paper proposed a novel scheme of energy-efficient compressed sensing framework for processing the acceleration-based data acquired from wireless body area networks (WBANs), in order to save the energy of WBANs-based system. With the optimal scheme of sparse binary matrices, the raw accelerometer data with nonsparse is compressed by linearly projected at sensor node before their transmission, and then the compressed data is reconstructed by the novel block Bayesian learning algorithm (BSBL) at remote terminal. The acceleration data from USC-HAD dataset of Southern California was used to evaluate the effectiveness of our proposed technique. The experimental results showed that the optimal scheme of sparse binary matrix could obtain the same reconstruction error (0.0045) as Gaussian or Bernouilli random matrix when a number of nonzero values were selected as 6 in each column of the designed sparse binary matrix and the ratio of compression was 50%. Besides, compared with the traditional CS-based reconstruction algorithms, our proposed BSBL algorithm for reconstruction of acceleration data could increase by 17 dB of signalnoise ratio, significantly improving the performance of reconstruction of acceleration data. These results suggested that, with the optimal design of sparse binary matrix, the designed compressed sensing framework could acquired the acceleration data at sub-Nyquist sampling rate and greatly reduce the number of transmitted data by simple linear transform at sensor node for saving energy. It also can contribute to improving the performance of reconstruction of non-sparse acceleration data by using BSBL. Our work can provide a novel approach for further practical implementation such as the design of simple hardware of sensor node, improvement of the performance of reconstruction of accelerationdata and the development of WBANs\|based system with lower energy consumption for remote monitoring physical activity.
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