  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\%.
