## 4 Overview of Included Studies
The DT updates the item's size, shape, and temperature, enabling users to confirm the item's accuracy and condition. Another related work is [67] where the authors proposed a novel framework in the healthcare application field, named PP-SPA for privacy-preserved Human Activity Recognition (HAR) and real-time support for cognitive impaired individuals using smartphone-based virtual personal assistants. Through a custom smartphone application developed for Android, which continuously collects data from the smartphone's sensors, they were able to track the user's daily life activities and assist them if needed. The app also processes the collected data remotely using web services. A digital diary feature is integrated into the app, which contains a dictionary of activities. When the app detects a user's location and activity, it provides auditory prompts to guide the user in executing tasks. Various machine learning algorithms were employed, and an accuracy of 90% in activity recognition was achieved. Specifically, the Hoeffding Tree algorithm and Logistic Regression outperformed Random Forest and Naive Bayes by significant margins in all performance metrics used. The PP-SPA framework effectively improves the routine life of cognitively impaired individuals by providing personalized health assessments and recommendations. The challenges identified in this work include the need for ensuring user privacy and the need for accurate real-time activity recognition in a home environment. The study could be benefited from a wider and bigger dataset, as well as testing in a real world multi-resident home. Continuing in the realm of healthcare, [PERSON] and [PERSON] in their 2022 research [68] developed a smart human-centric medical digital twin (MDT) focusing on lung biopsy that effectively combines AI, VR, and IoT. The findings underscore the potential of these technologies to revolutionize clinical processes and enhance medical training. The MDT functions as a virtual patient replica, supported by a dataset created from 3 real hospitals and a customized CNN for anomaly detection and treatment guidance, though the study does not detail its accuracy or compare results with human diagnoses. The study also involved the creation of a VR-based surgery simulator, VatsSim-XR, which integrates VR, AR (augmented reality), and MR (mixed reality) modes for clinical training in lung biopsy procedures. The simulator comprises a laptop, Oculus VR headsets, two positioners and two force feedback devices to add haptic feedback and improve virtual operation realism. The experiments carried out demonstrated that the MDT significantly improved novice doctors' surgical skills toward expert levels. Although fully virtual training offered a wider field of vision, expert doctors did not perform significantly better than novice ones in some cases. This suggests that while VR and AR have their advantages, they may not always yield better outcomes compared MR. To address the risks of sharing private medical data via MDT, the authors also proposed a CodeBERT-based NN to detect and mitigate cybersecurity vulnerabilities. Their approach, which employs unique code processing and knowledge representation methods, showed a precision of 70%. However, the current accuracy requires refinement to meet the standards needed for practical deployment. Other key limitation include the study's reliance on data from only three hospitals in specific Chinese regions, limiting generalizability, and its narrow focus on lung biopsy procedures, restricting broader applications. The simulation environment lacks fidelity and could be enhanced. The study also omits evaluations of healthcare professionals' user experience, overlooking crucial metrics such as user satisfaction, learning curves, and long-term skill retention. Additionally, practical aspects such as system scalability, integration with existing hospital infrastructure, and cost implications remain unaddressed. Including these factors would offer a more comprehensive evaluation and support real-world implementations.