Wi-Fi signals can reveal patterns of human activity by analyzing how movement disturbs signal propagation. While prior work on Channel State Information (CSI) has shown promise for indoor activity recognition, it typically assumes sensor placement within the monitored environment or in adjacent rooms. This study investigates a more constrained and privacy-sensitive scenario: can activity inside a home be inferred using low-cost wireless devices placed entirely outside, with no interior access? Using a custom active sensing setup, we collected over 600 min of CSI data across two residential apartments, capturing room-level presence under varying wall materials, device placements, and participant behavior. We evaluated multiple learning strategies—including supervised, unsupervised, and semi-supervised models—to assess the feasibility of inferring interior activity from exterior signals. Results show that room-level localization is achievable even through thick residential walls, though performance varies substantially with environmental structure and sensor configuration. These findings demonstrate the viability of a previously unexplored form of passive activity inference and raise important questions about wireless privacy in domestic settings.