基于分布式压缩感知的可穿戴多传感数据联合重构新方法

为提高可穿戴多传感数据远程联合重构性能，提出了一种基于分布式压缩感知的可穿戴多传感加速度数据联合重构新方法。该方法首先对可穿戴多传感原始数据压缩编码，将数据融合传送至远端服务器；然后，基于可穿戴传感数据的时空相关性，构建块稀疏贝叶斯学习联合重构算法，实现压缩数据解码，准确重构各传感原始数据；最后，新方法对美国加州伯克利大学可穿戴多传感运动数据进行分析。实验结果表明，对不同编码采样率，文章所提方法重构性能明显优于传统的算法，并且能够准确解码压缩数据，有望在远程医疗环境下推广应用。

A Novel Distributed Compressed Sensing-Based Joint Reconstruction Method for Multiple Sensor Data from Wearable Device

In order to improve the performance of joint reconstruction of multi-sensor acceleration data from different wearable devices, a novel approach to jointly reconstruct based on distributed compressed sensing (DCS) algorithm was proposed. The basic idea was that the raw data was firstly compressed through encoding, and the encoded data was sent to remote terminal. Then, with the spatiotemporal correlation of data from sensors, the joint reconstruction method based on Block Sparse Bayesian Learning (BSBL) was applied to decode the compressed data at remote terminal. At last, the wearable data from University of California-Berkeley database was analized. Experiments show that the proposed approach can gain better performance than the traditional joint reconstruction algorithms such as TMSBL and tMFOCUSS, and decode the compressed data accurately. The proposed technique may be helpful for telemedicine application.