Innovative solutions have been conceptualized in the healthcare sector to increase patient life expectancy while decreasing healthcare costs. As a state-of-the-art innovation, Digital Twins (DT) have the potential to revolutionize healthcare. DT is creating a digital representation of a real-world object that represents the current state by dynamically updating its data. Conspicuously, to protect the confidentiality of health data, cutting-edge methods like the Internet of Things (IoT), discrete-time, fog computing, and blockchain technology are utilized to create a comprehensive context-aware physical activity monitoring framework for adult care. In the current research, deep learning's capacity to process data sequentially is used to examine an elderly person's motions to determine signs of abnormality. Furthermore, the proposed framework can secure individual data using the advanced security features of blockchain. The presented method can perform a timely and accurate analysis of a person's abnormal occurrence. Experimental simulation results have shown enhanced efficacy of DT with intelligent healthcare solutions that would aid in the development of efficient medical services. Specifically, the effectiveness of the proposed solution is evaluated in terms of its ability to identify anomalous events, data model, processing delay, and cost computation.