The ability to clearly communicate a wide range of emotional states is considered a desirable trait for social robots. This research proposes that the Geneva Emotion Wheel (GEW), a self-report instrument for measuring emotional reactions, has strong potential for use as a tool for evaluating the expression of affective content by robots. Factors that make the GEW advantageous over existing evaluation methods include: ease of administration, reduction in the importance of word labels, and coverage of "no emotion" states. Statistical analyses of the GEW are proposed, isolating quantitative metrics of emotion distinctness. An experiment requiring participants to rate the perceived emotion of a social robot was conducted, employing the proposed methods. Analysis using the GEW revealed significant differences in the reliability of different expressions to clearly convey emotional states. The GEW provided a repeatable, systematic framework for estimating perceived affect of robot expression. Thus, the results suggest the GEW offers a powerful tool for design purposes as well as analysis. To support future research using the GEW, the software used for the analysis has been packaged and made available as an open-source resource to the community.

Socially assistive robots (SARs) have the potential to improve working conditions of care workers, empower vulnerable people to retain independence, and even provide social companionship. Through a series of focus groups, this study explores how older adults and professional care workers in a Continued Care Retirement Community (CCRC) perceived a bespoke SAR platform known as Stevie. Using a mixed-method approach, it emerged that both care staff and residents developed a strong fondness for the robot, perceived it to be useful, and could envision a range of useful applications.