Zero-Fi: Zero-Shot Wi-Fi-Based Human Activity Recognition via Contrastive Signal-Language Alignment

Researchers propose Zero-Fi, a contrastive signal-language alignment framework that enables zero-shot Wi-Fi-based human activity recognition by aligning Wi-Fi signal features with natural-language activity descriptions in a shared embedding space, eliminating the need for labeled samples for new activity classes. Experiments on large-scale public benchmarks show effective recognition of held-out activity classes, extending Wi-Fi sensing beyond predefined categories.

arXiv:2607.26381v1 Announce Type: new Abstract: Wi-Fi-based human activity recognition has advanced substantially, but most existing methods assume a closed set of activities and require labeled Wi-Fi samples for every target class, limiting their ability to recognize unseen activities. We present Zero-Fi, a contrastive signal-language alignment framework for zero-shot Wi-Fi-based human activity recognition. Zero-Fi learns unified representations from complementary Wi-Fi signal features and aligns them with the semantic representations of natural-language activity descriptions in a shared embedding space. This cross-modal alignment enables Zero-Fi to recognize new activity classes without requiring labeled Wi-Fi samples or model adaptation for those classes. Experiments on large-scale public benchmark datasets demonstrate effective zero-shot recognition of held-out activity classes, highlighting the potential of signal-language alignment to extend Wi-Fi sensing beyond predefined activity classes.