Edge‐Centric Digital Twin Architecture for Real‐Time Patient Health and Activity Monitoring

Objectives Monitoring patient physical activities is essential for assessing health status, enabling timely clinical interventions, and supporting personalized healthcare management. Conventional cloud‐centric healthcare monitoring systems, however, suffer from high communication latency, excessive bandwidth consumption, and limited responsiveness when processing continuously generated wearable sensor data. This study proposes a Digital Twin‐enabled edge‐assisted healthcare monitoring framework that integrates wearable Internet of Things (IoT) devices, edge computing, Digital Twin technology, and deep learning‐based activity recognition to enable real‐time, scalable, and patient‐centric healthcare monitoring. Method The proposed framework employs continuously synchronized Digital Twins to maintain virtual representations of patients and wearable devices, enabling virtual–physical state alignment, contextual patient‐state assessment, healthcare decision support, and proactive alert generation. An edge‐assisted activity fusion mechanism based on Kalman filter data fusion enhances data quality while reducing communication overhead. In addition, a Bidirectional Gated Recurrent Unit (Bi‐GRU)‐based activity recognition model is utilized to capture temporal dependencies in wearable sensor data for accurate activity classification. The framework was validated using a real‐world activity dataset and the UCI‐HAR benchmark dataset, while system‐level performance was evaluated through iFogSim simulations. Results The proposed framework achieved activity recognition accuracies of 94.24% and 98.26% on the real‐world and UCI‐HAR datasets, respectively, outperforming CNN, RNN, LSTM, CNN‐LSTM, Temporal Convolutional Network (TCN), and Transformer‐based approaches. The Digital Twin layer attained 98.41% synchronization accuracy, 97.86% state consistency, 97.24% virtual–physical state alignment, and an average update latency of 0.47 s, demonstrating reliable synchronization between physical and virtual entities. Discussion The experimental findings demonstrate that the proposed Digital Twin‐enabled edge‐assisted framework effectively improves activity recognition accuracy while reducing communication latency and network overhead. The integration of edge intelligence with synchronized Digital Twins enhances resource utilization, maintains reliable virtual–physical synchronization, and supports timely healthcare decision‐making. These capabilities highlight the framework's potential for scalable, intelligent, and patient‐centric healthcare monitoring in next‐generation smart healthcare environments.