A sparse autoencoder compressed sensing method for acquiring the pressure array information of clothing

摘要

In this paper, we integrate some ideas of sparse autoencoder of deep learning into compressed sensing (CS) theory, and set up a sparse autoencoder compressed sensing (SAECS) model, which can improve the compressed sampling process of CS with compression of sparse autoencoder in deep learning. The original CS theory has no function of autonomic regulation, so we introduce the idea of sparse autoencoder of deep learning to improve CS theory. Then we calculate the error between the recovery output data and the input data. By judging the obtained error and the acceptable error, the SAECS model can choose autonomously the most appropriate sparsity and the most appropriate length of measurement vector. This SAECS model can then reconstruct the original signal that can satisfy the acceptable error requirement with the minimum length of measurement vector in CS theory. We investigate the effectiveness of the proposed method by using sampled pressure data from human body model. Experimental results demonstrate that, in comparison with the state-of-the-art methods for running time, our SACES approach can effectively decrease the running time to find the shortest measurement vector in the case of accepted error. (C) 2017 Elsevier B.V. All rights reserved.