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