Publications
カンファレンス (国際) Convolutional Compressed Sensing for Smartphone Acceleration Data Compression
Liqiang Xu (The University of Tokyo), Yuuki Nishiyama (The University of Tokyo), Masamichi Shimosaka (Tokyo Institute of Technology), Kota Tsubouchi, Kaoru Sezaki (The University of Tokyo)
The 20th ACM Conference on Embedded Networked Sensor Systems (SenSys 2022)
2022.11.6
As intelligent sensing and smartphone technologies have progressed, a huge amount of highly heterogeneous data have come to be stored in smartphones and uploaded to servers for analysis on a daily basis. This has led to vast storage overheads for users and companies. Remarkably, most of the collected data are just used when necessary; hence, data compression becomes the most efficient strategy for suppressing the increase in storage overhead. Compressed sensing (CS) technology is one approach to compressing data, but traditional CS-based algorithms are significantly time-consuming and have low reconstruction performance. In light of these drawbacks, this paper proposes a compressed sensing framework that instead takes advantage of the low time cost and adaptive learning capability of deep learning methods, wherein a convolutional neural network (CNN) is used for compressing and reconstructing acceleration data. Our experiments with actual smartphone acceleration data show that the proposed method dramatically improves the reconstruction performance with very little reconstruction time compared with traditional compressed sensing methods.