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http://dx.doi.org/10.7471/ikeee.2022.26.3.341

CALS: Channel State Information Auto-Labeling System for Large-scale Deep Learning-based Wi-Fi Sensing  

Jang, Jung-Ik (School of Computing, Gachon University)
Choi, Jaehyuk (School of Computing, Gachon University)
Publication Information
Journal of IKEEE / v.26, no.3, 2022 , pp. 341-348 More about this Journal
Abstract
Wi-Fi Sensing, which uses Wi-Fi technology to sense the surrounding environments, has strong potentials in a variety of sensing applications. Recently several advanced deep learning-based solutions using CSI (Channel State Information) data have achieved high performance, but it is still difficult to use in practice without explicit data collection, which requires expensive adaptation efforts for model retraining. In this study, we propose a Channel State Information Automatic Labeling System (CALS) that automatically collects and labels training CSI data for deep learning-based Wi-Fi sensing systems. The proposed system allows the CSI data collection process to efficiently collect labeled CSI for labeling for supervised learning using computer vision technologies such as object detection algorithms. We built a prototype of CALS to demonstrate its efficiency and collected data to train deep learning models for detecting the presence of a person in an indoor environment, showing to achieve an accuracy of over 90% with the auto-labeled data sets generated by CALS.
Keywords
Wi-Fi Sensing; Channel State Information; Auto-Labeling; Computer Vision; Deep Learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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