From User Recognition to Activity Counting: An Identity-Agnostic Approach to Multi-User WiFi Sensing

Abstract:WiFi Channel State Information (CSI) enables device-free human activity recognition, but multi-user methods typically assume a fixed set of known users and degrade on unseen individuals. We reformulate multi-user recognition as identity-agnostic activity counting, estimating how many users perform each activity without assigning actions to individuals. For a controlled comparison, identity-dependent predictions are converted to activity counts and evaluated with the same integer-valued metrics using a shared pipeline that differs only in output head and objective. On WiMANS, identity-agnostic counting achieves lower integer-count MAE than derived identity-dependent counts (0.0815 versus 0.0909 on a 0-5 count scale) despite using coarser supervision. This ordering holds across all eight environment-band conditions, while derived counts lead only in exact-match accuracy. Under leave-users-out evaluation, identity-dependent macro-F1 drops from 80.02% to 33.85%, its derived counts perform only slightly better than an all-zero predictor, and the exact-match advantage reverses, while identity-agnostic counting leads on all counting metrics. Silhouette analysis shows that identity-dependent embeddings cluster by user identity, whereas identity-agnostic embeddings cluster by activity composition. Activity counting therefore provides a more accurate and robust formulation for scene-level activity composition estimation without requiring a predefined user set.

10 pages, 5 figures. Revised version with updated experiments, analysis, and presentation

Machine Learning (cs.LG)

arXiv:2604.16572 [cs.LG]

(or arXiv:2604.16572v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2604.16572 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.