Robots of today are eager to leave constrained industrial environments and embrace unexplored and unstructured areas, for extensive applications in the real world as service and social robots. Hence, in addition to these new physical frontiers, they must face human ones, too. This implies the need to consider a human-robot interaction from the beginning of the design; the possibility for a robot to recognize users' emotions and, in a certain way, to properly react and "behave". This could play a fundamental role in their integration in society. However, this capability is still far from being achieved. Over the past decade, several attempts to implement automata for different applications, outside of the industry, have been pursued. But very few applications have tried to consider the emotional state of users in the behavioural model of the robot, since it raises questions such as: how should human emotions be modelled for a correct representation of their state of mind? Which sensing modalities and which classification methods could be the most feasible to obtain this desired knowledge? Furthermore, which applications are the most suitable for the robot to have such sensitivity? In this context, this paper aims to provide a general overview of recent attempts to enable robots to recognize human emotions and interact properly.

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Analyses of user experience in the electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that self-reporting alone carries inherent problems—mainly the misinterpretation and temporal delay during longer experiments—and therefore, should not be used as a sole metric. To tackle this problem, we propose the possibility of modeling consumer experience using psychophysiological measures and demonstrate how such models can be trained using machine learning methods. We use a machine learning approach to model user experience using real-time data produced by the autonomic nervous system and involuntary psychophysiological responses. Multiple psychophysiological measures, such as heart rate, electrodermal activity, and respiratory activity, have been used in combination with self-reporting to prepare training sets for machine learning algorithms. The training data was collected from 31 participants during hour-long experiment sessions, where they played multiple video-games. Afterwards, we trained and compared the results of four different machine learning models, out of which the best one produced ∼96% accuracy. The results suggest that psychophysiological measures can indeed be used to assess the enjoyment of digital entertainment consumers.

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