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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. [ABSTRACT FROM AUTHOR]

Confined spaces represent a critical issue for the safety of workers. Several accidents, often fatal, occurred in the process industry, caused by inadequate education and training about the possible formation of toxic and/or explosive atmospheres generated by the variation of environmental parameters such as temperature, or by the working operations, which may involve the handling of hazardous impurities. The present study proposes a quantitative methodology to support the identification of hazards in process equipment considering the subsequent assessment of risks to which workers are exposed and suggests the preparation of appropriate working procedures. The proposed methodology, starting from the specific site data, allows the operators to predict the parameters needed for the quantitative evaluation of the possible hazards and the associated risks arising from the operations carried out in the process equipment. The methodology is tailored to the specific case of the food industry, and particularly edible oil refining storage. Based on the outcomes of the method, besides determining a hazard ranking of the considered operation, it is possible to establish the specific training requirements of operators, the appropriate personal protective equipment to be used, and, finally, the working procedure. Finally, in order to apply the results obtained, case studies based on the analysis of realistic industrial plants are presented and discussed. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) systems help users perform tasks and operations in man–machine interaction, by adding virtual information (such as live-video streams, pictures or instructions) to the real-world environment. This paper describes the design and testing of an AR solution created to enhance the safety of employees when carrying out maintenance tasks on a food processing machine. The machine which was analyzed is a hot-break juice extractor used to obtain juice from fruits and vegetables by separating out seeds and peel. The maintenance task for which the AR system is intended involves cleaning the machine’s porous sieves or substituting them with clean replacements and should be carried out at least every 12 hours while the plant is in operation. The paper discusses the main steps involved in developing the AR solution, its testing in the real operating environment and the expected pros/cons of its implementation and use. [ABSTRACT FROM AUTHOR]