Consensus-Based Distributed Channel State Information for Human Activity Recognition

Wi-Fi-based human activity recognition is promising but often limited by costly retraining and poor scalability. This work introduces a consensus-based framework for distributed CSI sensing, enabling robust and scalable activity recognition with minimal training and communication overhead. Transmitter–receiver pairs are ranked based on their short training momentum and allocated by a central coordinator (e.g., a router) to monitor specific locations. Experiments across three locations and twelve participants show that a location-aware consensus approach matches optimally placed solutions (F1 =0.98) while improving robustness and temporal stability during dynamic activity flows.