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Abstract-The hand and eye coordination of the surgeon is one of the most important factor minimally invasive thoracic surgery. Virtual Simulation is a powerful training tool in surgery. At present, the main problems of the learning process are to reduce the learning time and to improve specific skills by the simulation. There are different options to perform tests: working with animals, with artificial tissues to simulate the pressure, and even simulators through joystick. The main disadvantage is the high cost of this systems and the complexity to replicate the situation during a real operations. In this paper, a virtual reality system is presented. This system allows to reduce the learning time and to improve the hand and eye coodination of the surgeon. Moreover, it is a low cost, portable and easy-to-use solution for future surgeons.

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Abstract-Communication with other people and their environment, is an essential skill for people with special needs. In this study we present a communicator which interprets a series of commands by means of corporal expressions. These body expressions are learned by a gesture recognition system according to the requirements and disability of the user. Each of the commands adapt themselves to daily tasks. The system learn gestures and associates them with concrete actions that the user wants to do or needs at that moment.

Gesture recognition is an ideal means of interaction because it allows users not to have to make contact with any surface, which is a safe and hygienic means, especially in the pandemic situation that is occurring worldwide. However, gesture recognition is not a new discipline and it has been researched for many years but this type of interaction has not succeeded in replacing the keyboard and mouse. It is very useful to know about the advances that are being made with artificial intelligence in gesture recognition to be able to perform a more robust and reliable gesture recognition with a low response time. As it is, deep learning is being integrated into various areas to increase improvement in performance and one such area is artificial intelligence. In this way, there is the possibility that in the future the recognition of gestures will be a viable option as a means of daily interaction for the user and the main objective of this paper is to contribute to that process. For this reason, this study has analyzed 571 papers related to gesture recognition and artificial intelligence. This analysis has extracted relevant information related to scientific production, such as the most productive authors and journals or the most pertinent articles on the subject. Furthermore, we have developed our own model, which shows the relationship between the types of gesture recognition and the artificial intelligence techniques that have been applied for this task.