Determining the affective body language of older adults during socially assistive HRI

Our research focuses on the development of a socially assistive robot to provide cognitive and social stimulation during meal-time scenarios in order to promote proper nutrition amongst the elderly. In this paper, we present the design of a novel automated affect recognition and classification system that will allow the robot to interpret natural displays of affective human body language during such one-on-one assistive scenarios. Namely, we identify appropriate body language features and learning-based classifiers that can be utilized for accurate affect estimation. A robot can then utilize this information in order to determine its own appropriate responsive behaviors to keep people engaged in this crucial activity. One-on-one assistive meal-time experiments were conducted with the robot Brian 2.1 and elderly participants at a long-term care facility. The results showed the potential of utilizing the automated affect recognition and classification system to identify and classify natural affective body language features into valence and arousal values using learning-based classifiers. The elderly users displayed a number of affective states, further motivating the use of the affect estimation system.