Recent studies have shown that cognitive and social interventions are crucial to the overall health of older adults including their psychological, cognitive, and physical well-being. However, due to the rapidly growing elderly population of the world, the resources and people to provide these interventions is lacking. Our work focuses on the use of social robotic technologies to provide person-centered cognitive interventions. In this article, we investigate the acceptance and attitudes of older adults toward the human-like expressive socially assistive robot Brian 2.1 in order to determine if the robot's human-like assistive and social characteristics would promote the use of the robot as a cognitive and social interaction tool to aid with activities of daily living. The results of a robot acceptance questionnaire administered during a robot demonstration session with a group of 46 elderly adults showed that the majority of the individuals had positive attitudes toward the socially assistive robot and its intended applications.

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In Human-Robot Interactions (HRI), robots should be socially intelligent. They should be able to respond appropriately to human affective and social cues in order to effectively engage in bi-directional communications. Social intelligence would allow a robot to relate to, understand, and interact and share information with people in real-world humancentered environments. This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings. Human-affect detection from facial expressions, body language, voice, and physiological signals are investigated, as well as from a combination of the aforementioned modes. The automated systems are described by their corresponding robotic and HRI applications, the sensors they employ, and the feature detection techniques and affect classification strategies utilized. This paper also discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI.

As people get older, their ability to perform basic self-maintenance activities can be diminished due to the prevalence of cognitive and physical impairments or as a result of social isolation. The objective of our work is to design socially assistive robots capable of providing cognitive assistance, targeted engagement, and motivation to elderly individuals, in order to promote participation in self-maintenance activities of daily living. In this paper, we present the design and implementation of the expressive human-like robot, Brian 2.1, as a social motivator for the important activity of eating meals. An exploratory study was conducted at an elderly care facility with the robot and eight individuals, aged 82-93, to investigate user engagement and compliance during mealtime interactions with the robot along with overall acceptance and attitudes towards the robot. Results of the study show that the individuals were both engaged in the interactions and complied with the robot during two different meal-eating scenarios. A post-study robot acceptance questionnaire also determined that, in general, the participants enjoyed interacting with Brian 2.1 and had positive attitudes towards the robot for the intended activity.

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