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.

Back pain causes more global disability than any other health problem studied and the number of patients is growing. In Europe and in the US it is the number one cause of lost work days. This paper propounds a new approach by exploring the effect of utilizing a humanoid robot as a therapy-assistive tool in educating children to perform back exercises designed by a professional therapist. In our previous research a NAO robot was programmed and employed as a robotic assistant to a human physiotherapist to perform exercises in an elementary school in Slovakia. This paper goes further in designing a Wizard of Oz, where the exercises can be controlled and intervened by motivational behaviors of the robot (emotional expressions). Currently we are developing a system based on reinforcement learning that should adopt the motivational interventions from the Wizard. The promising results of this study in the physical therapy suggest the effective future use of social robots in reducing the symptoms of the most extended global disability in the world.