## 4 Overview of Included Studies  
This section offers a comprehensive overview of the studies selected for the systematic review. The Table 1 details the key characteristics of the included articles, offering a snapshot of the works examined in this review. By organizing data across various dimensions such as application fields, technological combinations, implementation types, and methodological approaches, the table aims to provide a structured insight into the current landscape of interdisciplinary research. Additionally, it offers a comparative perspective to address the research questions and identify trends in combining VR, AI, and IoT across various fields. The columns highlight not only the technological innovations but also the practical aspects of each study, including tools and methodologies used, technological advantages, inherent challenges, and significant research results. [PERSON] et al. in their 2020 research [65] explore the integration of AI, VR, and IoT in wearable electronics, focusing on gait analysis for healthcare applications. They fabricated a pair of socks able to harvest energy through tactile triboelectric nanogenerators (TENGs) and collect data in a non-invasive way. The smart socks were able to collect  
Figure 1: PRISMA flowchart illustrating the procedure undergone by the papers included in this systematic review. features and feed a one-dimensional convolutional neural network (1D CNN) trained on data from five participants. This AIoT pairing is particularly useful for analyzing walking patterns in individuals with conditions such as Parkinson's disease, diabetes, and musculoskeletal abnormalities. The system also incorporated a micro-controller unit (MCU) to transmit data to computers, enabling emergency alerts for irregular gait signals, such as falls, and then inform caregivers. A challenge that the authors came up against during the creation of the smart socks was that the accuracy was largely reduced as the number of participants increased, while the sensor's accuracy was depended on weather and environment conditions. However, the authors mention that with sufficient training time and an optimal sliding window size, accuracy can be improved. The final system included a triple-sensor sock, a signal pre-processing circuit, an MCU with wireless transmission, a personal computer, and a VR environment created with the Unity engine. The article also showcased its applications in VR fitness games where the users can control characters in 3D worlds but also in smart home environments where the system distinguished family members from strangers solely based on walking patterns. This article shows how much can be done with simple low-cost tools thanks to the combination of IoT, AI and VR. [PERSON] et al. in [66] present a DT-based virtual shopping system utilizing an AIoT-enabled soft robotic manipulator, which also integrates L-TENGs as robotic fingers, T-TENGs and polyvinylidene fluoride (PVDF) sensors for enhanced object recognition and user interactions. This system was designed for use in retail and industrial settings where through IoT, AI and VR it would be able to assist virtual shopping through item identification and handling. It recognizes objects through the sensors which extract features later processed by AI algorithms such as SVM and a three-layer 1D-CNN. To evaluate the system, a dataset of objects was created and testing with real simulations was conducted demonstrating an impressive object recognition accuracy of 97.14% with the main limitation being the recognition of similarly shaped objects. Later, with the use of the PVDF sensor, a temperature feature could also be extracted to help in the classification process after the size and shape characteristics were extracted. To demonstrate the system's capabilities, a DT virtual shop was proposed where users could shop in the virtual world while a robotic manipulator on a moving cart mimicked their actions in the real shop, recognizing and collecting items.