Publications
カンファレンス (国際) Deep Learning-Based Compressed Sensing for Mobile Device-Derived Sensor Data
Liqiang Xu (The University of Tokyo), Yuuki Nishiyama (The University of Tokyo), Kota Tsubouchi, Kaoru Sezaki (The University of Tokyo)
The 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024)
2024.10.24
As smart sensing and mobile technologies advance, storing diverse sensor data on smartphones and cloud servers becomes challenging. Effective data compression is crucial. Traditional compressed sensing (CS) methods often struggle with the unique characteristics of sensor data—like variability, dynamic changes, and different sampling rates—leading to slow processing and poor reconstruction quality. To address these issues, we developed Mob-ISTA-1DNet, an innovative CS framework that integrates deep learning with the iterative shrinkage-thresholding algorithm (ISTA) to adaptively compress and reconstruct smartphone sensor data. This framework is designed to manage the complexities of smartphone sensor data, ensuring high-quality reconstruction across diverse conditions. We developed a mobile application to collect data, including accelerometer, gyroscope, barometer and other sensor measurements, from 30 volunteers. Comparative analysis reveals that Mob-ISTA-1DNet not only enhances reconstruction accuracy but also significantly reduces processing time, consistently outperforming other methods in various scenarios.
Paper :
Deep Learning-Based Compressed Sensing for Mobile Device-Derived Sensor Data
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