Introducing robots in healthcare facilities and homes may reduce the workload of healthcare personnel while providing the users with better and more available services. It may also contribute to interactions that are engaging and safe against transmitting contagious diseases for senior adults. A major challenge in this regard is to design and adapt the robot’s behavior based on the requirements and preferences of the different users. In this paper, we report a conducted use study on how people perceive different kinds of robot encounters. We had two groups of target users: one with senior residents at a care center and another with young students at a university, which would be representative for the visitors and care volunteers in the facility. Several common scenarios have been created to evaluate the perception of the robot’s behavior by the participants. Two sets of questionnaires were used to collect feedback on the behavior and the general perception of the users about the robot´s different styles of behavior. An exploratory analysis of the effect of age shows that the age of the targeted user group should be considered as one of the main criteria when designing the social parameters of a care robot, as seniors preferred slower speed and closer distance to the robot. The results can contribute to improving a future robot’s control to better suit users from different generations.

Earlier research [1] suggests that a social robot can contribute to gaining self-management skills for children with diabetes. In the current study, the positive effect of different robot characteristics of a social robot is investigated in a natural environment with end-users. The NAO robot played an interactive trivia game with eight children with diabetes, which was video-recorded. After the study children filled in a questionnaire and parents were interviewed. Combining the data provided several findings: movements of the robot were entertaining, not all children appreciated eye contact and expressions contributed to bonding. These results can be used to improve interactions between children and human-like robots.