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As technology advances, we are surrounded by more complex products that can be challenging to use and troubleshoot. We often turn to online resources and the help of others to learn how to use a product’s features or fix malfunctions. This is a common issue in both everyday life and industry. The key to being able to use a product or fix malfunctions is having access to accurate information and instructions and gaining the necessary skills to perform the tasks correctly. This paper offers an overview of how artificial intelligence, digital twins, and the metaverse—currently popular technologies—can enhance the process of acquiring knowledge, know-how, and skills, with a focus on industrial maintenance. However, the concepts discussed may also apply to the maintenance of consumer products.

The issue of training operators in the use of machinery is topical in the industrial field and in many other contexts, such as university laboratories. Training is about learning how to use machinery properly and safely. Beyond the possibility of studying manuals to learn how to use a machine, operators typically learn through on-the-job training. Indeed, learning by doing is in general more effective, tasks done practically are remembered more easily, and the training is more motivating and less tiresome. On the other hand, this training method has several negative factors. In particular, safety may be a major issue in some training situations. An approach that may contribute overcoming negative factors is using Virtual Reality and digital simulation techniques for operators training. The research work presented in this paper concerns the development of a multisensory virtual reality application for training operators to properly use machinery and personal protective equipment (PPE). The context selected for the study is a university laboratory hosting manufacturing machinery. The application allows user to navigate the laboratory, to approach a machine and learn about how to operate it, and also to use proper PPE while operating a machine. Specifically, the paper describes the design and implementation of the application and presents the results of preliminary testing sessions.

The paper describes the design of a wearable and wireless system that allows the real-time identification of some gestures performed by basketball players. This system is specifically designed as a support for coaches to track the activity of two or more players simultaneously. Each wearable device is composed of two separate units, positioned on the wrists of the user, connected to a personal computer (PC) via Bluetooth. Each unit comprises a triaxial accelerometer and gyroscope, a microcontroller, installed on a TinyDuino platform, and a battery. The concept of activity recognition chain is investigated and used as a reference for the gesture recognition process. A sliding window allows the system to extract relevant features from the incoming data streams: mean values, standard deviations, maximum values, minimum values, energy, and correlations between homologous axes are calculated to identify and differentiate the performed actions. Machine learning algorithms are implemented to handle the recognition phase.