Cyber-Physical Healthcare Systems Using AI for Continuous Vital Sign Monitoring

Abstract

Cyber-physical healthcare systems (CPHS) allow sustained vital sign monitoring through the integration of physiological sensors, intelligent computing, and networked control, addressing the increasing demand for proactive and personalized healthcare. The conventional tools of monitoring are still bound to the data acquisition process that took intermittent values, false-alarm percentage, and inability to adjust to the dynamic patient status. To address these issues, this study suggests an AI-based CPHS system of real-time measurements and early warning against potential risks of such vital signs as heart rate, blood pressure, oxygen saturation, and respiratory rate. The main goal is to create a scalable, intelligent system, which can accurately, continuously, and contextually measure health. The suggested methodology is multimodal wearable sensing, used together with edge-cloud intelligence, in which sophisticated AI models are used to process noise-resilient signals, extract temporal features, and make predictive analytics. The analysis is done using four sophisticated algorithms, Temporal Fusion Transformer (TFT) to predict long-term vital trend, Graph Neural Networks (GNN) to learn inter-vital physiological relationships, Federated Learning-based CNN-BiLSTM to make privacy-preserving personalized predictions. It shows better results by experimental assessment on benchmark physiological datasets with up to 97.4 percent classification, 95.1 percent sensitivity, and a 38 percentage decline in false alarms against the traditional deep learning models. Latency result indicates the real time inference using 42ms at the edge layer. The findings affirm that AI-based CPHS can significantly improve reliability, responsiveness, and clinical decision support and thus can be used to conduct the continuous monitoring of smart hospitals and remote care of patients.