论文标题
很少有sense，使用几乎没有射击的学习，朝着可扩展的跨域Wi-Fi传感系统
FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning
论文作者
论文摘要
Wi-Fi传感可以对人类活动进行分类，因为每个活动都会导致通道状态信息（CSI）的独特变化。现有的WiFi传感遇到了有限的可扩展性，因为每当添加新活动时，都需要重新训练系统，这会导致数据收集和再培训的开销。跨域传感可能会失败，因为当涉及不同的环境或用户（域）时，活动与CSI变化之间的映射被破坏。本文提出了一些基于学习的WiFi传感系统，名为Liresense，该系统可以在只有很少的样本中识别出在看不见的域中的新颖类。具体而言，使用源域数据进行了特征提取器的离线预训练。当将系统应用于目标结构域时，很少使用样品来微调提取器以进行域的适应性。通过计算余弦相似性来进行推断。很少有sense可以通过从多个接收器的合作融合推理来进一步提高分类精度。我们使用三个公共数据集（即Signfi，widar和Wiar）评估了性能。结果表明，在不看到的域中，有五杆学习识别出的新颖类，精度为90.3 \％，96.5 \％，82.7 \％的Signfi，widar和WIAR数据集的精度为82.7 \％。我们的协作传感模型将系统性能平均提高了30 \％。
Wi-Fi sensing can classify human activities because each activity causes unique changes to the channel state information (CSI). Existing WiFi sensing suffers from limited scalability as the system needs to be retrained whenever new activities are added, which cause overheads of data collection and retraining. Cross-domain sensing may fail because the mapping between activities and CSI variations is destroyed when a different environment or user (domain) is involved. This paper proposed a few-shot learning-based WiFi sensing system, named FewSense, which can recognise novel classes in unseen domains with only few samples. Specifically, a feature extractor was pre-trained offline using the source domain data. When the system was applied in the target domain, few samples were used to fine-tune the feature extractor for domain adaptation. Inference was made by computing the cosine similarity. FewSense can further boost the classification accuracy by collaboratively fusing inference from multiple receivers. We evaluated the performance using three public datasets, i.e., SignFi, Widar, and Wiar. The results show that FewSense with five-shot learning recognised novel classes in unseen domains with an accuracy of 90.3\%, 96.5\% ,82.7\% on SignFi, Widar, and Wiar datasets, respectively. Our collaborative sensing model improved system performance by an average of 30\%.
