A novel breed of robots known as socially assistive robots is emerging. These robots are capable of providing assistance to individuals through social and cognitive interaction. However, there are a number of research issues that need to be addressed in order to design such robots. In this paper, we address one main challenge in the development of intelligent socially assistive robots: The robot's ability to identify human non-verbal communication during assistive interactions. In particular, we present a unique non-contact and non-restricting automated sensor-based approach for identification and categorization of human upper body language in determining how accessible a person is to the robot during natural real-time human-robot interaction (HRI). This classification will allow a robot to effectively determine its own reactive task-driven behavior dur-
Studies have shown that cognitive and social stimulation is crucial to the overall health of older adults including psychological, cognitive and physical well-being. However, activities to promote such stimulation are often lacking in long-term care facilities. Our work focuses on the use of social robotic technologies to provide person-centered cognitive interventions. Namely, this paper presents an HRI study with the unique human-like socially assistive robot Brian 2.1, in order to investigate the use and acceptability of the expressive human-like robot by older adults living in a longterm care center. Current studies with social robots for the elderly have been mainly directed towards collecting data on the acceptance and use of animal-like robots. Herein, we aim to determine if the robot's human-like assistive and social characteristics result in the elderly having positive attitudes towards the robot as well as accepting it as an interactive cognitive training tool.
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