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.

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