ArticleSensors (Basel, Switzerland)2025
A Machine Learning Approach for Estimating Person Counts Using Anonymous WiFi Data in a University Library.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
Accurately estimating indoor occupancy is essential for managing building spaces and infrastructure, with applications ranging from ensuring safe distancing and adequate ventilation during health crises to optimizing energy consumption and resource allocation. However, no existing technology simultaneously achieves accuracy, low-cost, and privacy preservation in indoor occupancy measurement. This study investigates the use of existing WiFi infrastructure as a non-intrusive sensing system, where access points operate as soft sensors that passively collect anonymized connection metadata serving as proxies for human presence. The proposed approach was validated in a university library over eight months, training supervised machine learning regression models on WiFi data and comparing predictions against computer-vision ground truth. The best-performing models (SVR, Ridge, and MLP) consistently achieved R
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