An AI-enabled secure framework for enhanced elder healthcare

The healthcare sector has been revolutionized by Information and Communication Technology (ICT), leading to increased patient life expectancy and reduced healthcare costs. In the realm of cutting-edge research, Digital Twins (DT) technology holds great promise for improving healthcare. This study introduces a new approach to securing adult healthcare data by combining the Internet of Things (IoT), DT technology, and blockchain technology. Specifically, a context-aware physical activity monitoring framework is proposed for adult healthcare, incorporating an Artificial Intelligence-inspired Convolutional Neural Network (CNN) technique to analyze real-time abnormalities in the elderly. The CNN is trained on a large dataset, learning to recognize patterns and anomalies associated with abnormal conditions or behaviors in the elderly. The framework also ensures data security through the advanced features of blockchain, employing the Reputation-based Byzantine Fault Tolerance (RBFT) method for the consortium network. Experimental simulations validate the proposed technique, demonstrating its superior efficacy compared to state-of-the-art techniques. The results exhibit betters statistical measures of Delay Latency (121.23s), Prediction Efficacy (Precision (93.357%), Specificity (93.58%), Sensitivity (94.15%), and F-measure (94.58%)), Reliability (89.62%), and Stability (71%).