Digital-Twin-Assisted Healthcare Framework for Adult

Medical professionals have devised novel solutions to transform the healthcare industry. Modern technology of digital twins (DTs) can revolutionize medical treatment significantly. The DT technology incorporates digitizing physical entities by constantly monitoring their current status. Conspicuously, a state-of-the-art secure framework for monitoring adults' physical activity is formulated using the culmination of the DT technology with Internet of Things (IoT)-edge computing, and blockchain technology. The presented framework is designed to discreetly secure the health data of the individual. To identify healthcare vulnerabilities in adults, the present study employs deep learning's ability to analyze IoT data sequentially. Specifically, a deep learning-assisted multilayered convolutional neural networks (CNNs) and long short-term memory (LSTM) technique is proposed for real-time vulnerability assessment. Additionally, the proposed framework can protect personal healthcare data by using the blockchain technique. For performance validation, numerous simulations were performed over the challenging data set. Based on the results, the proposed methodology can outperform state-of-the-art techniques by registering enhanced values of Temporal Delay Efficacy (120.79 s), Prediction Efficacy (Accuracy (92.24%), Specificity (94.67%), Sensitivity (95.26%), and F-measure (95.69%)), Reliability (91.58%), and Stability (64%).