论文标题
基于无线电的人类感测深度学习：最新进步和未来的方向
Deep Learning for Radio-based Human Sensing: Recent Advances and Future Directions
论文作者
论文摘要
尽管长达十年的研究清楚地证明了许多人类传感任务的巨大潜力（RF），但将这项技术扩展到大型方案中仍然存在传统方法的问题。最近，研究人员成功地应用了深度学习，将基于无线电的传感提高到了新的水平。已经提出了许多不同类型的深度学习模型，以在大型人群和活动集以及在看不见的环境中实现高感应的准确性。深度学习还使人们能够发现以前无法进行的新型人类传感现象。在这项调查中，我们对基于深度学习的RF感应的最新研究工作提供了全面的审查和分类法。我们还识别并比较了几个公开发布的标记的RF传感数据集，这些数据集可以促进这种深度学习研究。最后，我们总结了学习的经验教训，并讨论了基于深度学习的RF感应的当前局限性和未来方向。
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.
