Deep Learning for Radio-based Human Sensing: Recent Advances and Future Directions

Abstract

While decade-long research has clearly demonstrated the vast potential of radio frequency (RF) for many human sensing tasks, scaling this technology to large scenarios remained problematic with conventional approaches. Recently, researchers have successfully applied deep learning to take radio-based sensing to a new level. Many different types of deep learning models have been proposed to achieve high sensing accuracy over a large population and activity set, as well as in unseen environments. Deep learning has also enabled detection of novel human sensing phenomena that were previously not possible. In this survey, we provide a comprehensive review and taxonomy of recent research efforts on deep learning based RF sensing. We also identify and compare several publicly released labeled RF sensing datasets that can facilitate such deep learning research. Finally, we summarize the lessons learned and discuss the current limitations and future directions of deep learning based RF sensing.

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Cited by in corpus (4)

• Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration
• An Overview of Algorithms for Contactless Cardiac Feature Extraction from Radar Signals: Advances and Challenges
• BeamSense: Rethinking Wireless Sensing with MU-MIMO Wi-Fi Beamforming Feedback
• KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment