Since the development of smart healthcare services, different solutions have been developed in the field of healthcare to increase the life expectancy of the patient by reducing the cost of healthcare. Digital Twin (DT) is considered one of the most promising technologies and a game changer in the field of healthcare. DT is generating a virtual imitation of a physical object that mimics the status of an event by changing the information in real time. In this article, a smart context-aware physical activity monitoring framework is developed by combining different advanced techniques such as IoT, DT, FoT, CoT, and Blockchain to maintain the sensitiveness of the healthcare domain. In the proposed study, the physical movements of an elder are analyzed by utilizing the sequential data processing capability of deep learning to detect irregular physical events. In addition, the proposed framework can keep the data of an individual secured by applying progressed security highlights of blockchain. The proposed solution effectively analyzed an irregular event of an individual with considerable accuracy in real time. The calculated outcomes have shown the effectiveness of DT with smart healthcare solutions that would help to develop effective medical services by bringing patients and medical care experts together. Furthermore, the performance of the proposed solution is measured with respect to irregular event recognition, model training and testing, rate of latency, and data processing cost. In this manner, a case study defines the effectiveness of the proposed methodology in the smart healthcare industry.