A Distributed Adaptive Multi‐Agent Transformer Framework for Smart Home Activity Recognition in Elderly Care

Population aging has increased the demand for intelligent smart home systems for elderly care. Although Internet of Things (IoT) sensors enable unobtrusive residential monitoring, existing human activity recognition methods often rely on centralized processing and have limited ability to model heterogeneous sensing sources and long‐range temporal dependencies. To address this issue, this paper proposes a Distributed Adaptive Multi‐Agent Transformer (DAMAT) framework for smart home activity recognition. DAMAT models heterogeneous sensing streams as collaborative agents, captures long‐range temporal and cross‐agent contextual dependencies through transformer‐based interaction, and employs adaptive coordination attention to regulate agent contributions under different activity contexts. Experiments on the CASAS and UCI HAR datasets show that DAMAT consistently outperforms representative deep learning baselines. In particular, the CASAS results directly support the effectiveness of the proposed framework in distributed smart home sensing environments, while the UCI HAR results provide auxiliary evidence that the temporal modeling and adaptive coordination mechanisms remain effective on wearable inertial sensor data.