Experiencing stress during training is a way to prepare professionals for real-life crises. With the help of feedback tools, professionals can train to recognize and overcome negative effects of stress on task performances. This paper reports two studies that empirically examined the effect of such a feedback system. The system, based on the COgnitive Performance and Error (COPE) model, provides its users with physiological, predicted performance and predicted error-chance feedback. The first experiment focussed on creating stressful scenarios and establishing the parameters for the predictive models for the feedback system. Participants (n=9) performed fire-extinguishing tasks on a virtual ship. By altering time pressure, information uncertainty and consequences of performance, stress was induced. COPE variables were measured and models were established that predicted performance and the chances on specific errors. In the second experiment a new group of participants (n=29) carried out the same tasks while receiving eight different combinations of the three feedback types in a counterbalanced order. Performance scores improved when feedback was provided during the task. The number of errors made did not decrease. The usability score for the system with physiological feedback was significantly higher than a system without physiological feedback, unless combined with error feedback. This paper shows effects of feedback on performances and usability. To improve the effectiveness of the feedback system it is suggested to provide more in-depth tutorial sessions. Design changes are recommended that would make the feedback system more effective in improving performances.

Social robots can comfort and support children who have to cope with chronic diseases. In previous studies, a "facial robot", the iCat, proved to show well-recognized emotional expressions that are important in social interactions. The question is if a mobile robot without a face, the Nao, can express emotions with its body. First, dynamic body postures were created and validated that express fear, happiness, anger, sadness and surprise. Then, fourteen children had to recognize emotions, expressed by both robots. Recognition rates were relatively high (between 68% and 99% accuracy). Only for the emotion "sad", the recognition was better for the iCat (95%) compared to the Nao (68%). Providing context increased the number of correct recognitions. In a second session, the emotions were significantly better recognized than during the first session for both robots. In sum, we succeeded to design Nao emotions, which were well recognized and learned, and can be important ingredients of the social dialogs with children.