Publication
This paper introduces a novel method for device-free human detection by leveraging existing wireless communication signals from 4G-long-term evolution (4G-LTE) systems. By utilizing the pervasive 4G-LTE signals, our approach enhances the efficiency and coverage of human presence detection compared to WiFi signal based approaches. A previously overlooked, but crucial human presence scenario involving subtle human activities is successfully addressed and detected. Effective human presence detection relies heavily on precise feature extraction from channel estimates and careful feature selection. Through a detailed analysis and comparison of features discussed in previous work, along with the introduction of new features, we develop a machine learning-based approach to identify the most effective features for detecting human presence. Our machine learning model, trained with these selected features, is tested across different buildings and various scenarios using a commercial 4G-LTE network. The results demonstrate that our selected features significantly enhance detection accuracy and robustness, outperforming features introduced in previous literature across diverse environments.