One of the key areas of application for social robots is healthcare, particularly for the elderly. To better address user needs, a study involving the humanoid robot NAO was conducted at the Municipal Care Center in Krakow, Poland, with the participation of 29 older adults. This participatory design study explored their attitudes toward robots and technology both before and after interacting with the robot. It also identified the most desirable applications of social robots that could simplify everyday life for the elderly.

Gestures play an integral role in human communication. Our research aims to develop a gesture understanding system that allows for better interpretation of human instructions in household robotics settings. We conducted an experiment with 34 participants who used pointing gestures to teach concepts to an assistant. Gesture data were analyzed using manual annotations (MAXQDA) and the computational methods of pose estimation and k-means clustering. The study revealed that participants tend to maintain consistent pointing styles, with one-handed pointing and index finger gestures being the most common. Gaze and pointing often co-occur, as do leaning forward and pointing. Using our gesture categorization algorithm, we analyzed gesture information values. As the experiment progressed, the information value of gestures remained stable, although the trends varied between participants and were associated with factors such as age and gender. These findings underscore the need for gesture recognition systems to balance generalization with personalization for more effective human–robot interaction.