Personalizing Intelligent Systems and Robots with Human Motion Data

According to Merhabian, more than 90% of human-human communication is non-verbal when expressing affects and attitudes. Further studies have shown that a large proportion of non-verbal communication can be attributed to posture and to gesture. They communicate information about action: intent, meaning, as well as information about internal states such as affects. Emotional understanding is a key for satisfying and successful interaction between two or more humans, it must also be true for human-robot interaction. In this paper we explore the importance of non verbal information and communication, typically motion data, and how it can be used to develop and to personalize intelligent systems and robots. First, we present and discuss our findings on the strong correlation between what humans feel during an unannounced interaction with a humanoid robot and their movements and attitudes. Then, we propose a framework that uses not only the kinematics information of movements but also the dynamics. We use the direct measure of the dynamics when available. If not we propose to compute the dynamics from the kinematics, and use it to understand human motions. Finally, we discuss some developments and concrete applications in the field of health care and HRI.