An empirical study on pointing gestures used in communication in household settings

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.