CAJ | 학술논문

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。
위제고가천대다전감수거원정연합중구성능，제출료일충기우분포식압축감지적가천대다전감가속도수거연합중구신방법。해방법수선대가천대다전감원시수거압축편마，장수거융합전송지원단복무기；연후，기우가천대전감수거적시공상관성，구건괴희소패협사학습연합중구산법，실현압축수거해마，준학중구각전감원시수거；최후，신방법대미국가주백극리대학가천대다전감운동수거진행분석。실험결과표명，대불동편마채양솔，문장소제방법중구성능명현우우전통적산법，병차능구준학해마압축수거，유망재원정의료배경하추엄응용。
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 ifrstly 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.