Human Posture Detection Using Channel State Information (CSI) Data

WiFi-based posture and presence detection employs widely available wireless signals to monitor human activities in a non-intrusive manner, presenting an affordable, scalable, and privacy-sensitive alternative to conventional sensing technologies. By analyzing changes in WiFi signal characteristics, such as Received Signal Strength Indicator (RSSI) and Channel State Information (CSI), this method supports diverse applications, including smart home automation, healthcare monitoring, and security enhancements. This study investigates the underlying principles, experimental methodologies, and challenges of WiFi based sensing, focusing on posture recognition through CSI data analyzed with advanced deep learning techniques. Experimental results indicate excellent accuracy in detecting fundamental postures, such as standing and sitting, within controlled environments, although performance diminishes in multi-user or complex settings. Strategies to improve system robustness, scalability, and ethical considerations are proposed, paving the way for practical adoption in real world scenarios.