In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space), while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation). During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms.

Recently, different solutions leveraging Internet of Things (IoT) technologies have been adopted to avoid accidents in agricultural working environments. As an example, heavy vehicles, e.g., tractors or excavators, have been upgraded with remote controls. Nonetheless, the community continues to encourage discussions on safety issues. In this framework, a localization system installed on remote-controlled farm machines (RCFM) can help in preventing fatal accidents and reduce collision risks. This paper presents an innovative system that exploits passive UHF-RFID technology supported by commercial BLE Beacons for monitoring and preventing accidents that may occur when ground-workers in RCFM collaborate in outdoor agricultural working areas. To this aim, a modular architecture is proposed to locate workers, obstacles and machines and guarantees the security of RCFM movements by using specific notifications for ground-workers prompt interventions. Its main characteristics are presented with its main positioning features based on passive UHF-RFID technology. An experimental campaign discusses its performance and determines the best configuration of the UHF-RFID tags installed on workers and obstacles. Finally, system validation demonstrates the reliability of the main components and the usefulness of the proposed architecture for worker safety.

Obstacle avoidance is a key aspect for any autonomous vehicles, and their usage in agriculture must overcome additional challenges such as handling interactions with agricultural workers and other tractors in order to avoid severe accidents. The simultaneous presence of autonomous vehicles and workers on foot definitely calls for safer designs, vehicle management systems and major developments in personal protective equipment (PPE). To cope with these present and future challenges, the “SMARTGRID” project described in this paper deploys an integrated wireless safety network infrastructure based on the integration of Bluetooth Low Energy (BLE) devices and passive radio frequency identification (RFID) tags designed to identify obstacles, workers, nearby vehicles and check if the right PPE is in use. With the aim of detecting workers at risk by scanning for passive RFID-integrated into PPE in danger areas, transmitting alerts to workers who wear them, tracking of near-misses and activating emergency stops, a deep analysis of the safety requirements of the obstacle detection system is shown in this study. Test programs have also been carried out on an experimental farm with detection ranging from 8 to 12 meters, proving that the system might represent a good solution for collision avoidance between autonomous vehicles and workers on foot.

Mixed Reality (MR) environments hold immense potential for inducing a sense of embodiment, where users feel like their bodies are present within the virtual space. This subjective experience has been traditionally assessed using subjective reports and behavioral measures. However, neurophysiological approaches offer unique advantages in objectively characterizing embodiment. This review article explores the current state of the art in utilizing neurophysiological techniques, particularly Electroencephalography (EEG), Photoplethysmography (PPG), and Electrodermal activity (EDA), to investigate the neural and autonomic correlates of embodiment in MR for out-of-the-lab applications. More specifically, it was investigated how EEG, with its high temporal resolution, PPG, and EDA, can capture transient brain activity associated with specific aspects of embodiment, such as visuomotor synchrony, visual feedback of a virtual body, and manipulations of virtual body parts. The potential of such neurophysiological signals to differentiate between subjective experiences of embodiment was discussed, with a particular regard to identify the neural and autonomic markers of early embodiment formation during MR exposure in real settings. Finally, the strengths and limitations of the neurophysiological approach in the context of MR embodiment research were discussed, in order to achieve a more comprehensive understanding of this multifaceted phenomenon.