Human-Machine and Human-Robot Interaction for Long-Term User Engagement and Behavior Change

Abstract

The nexus of in-home intelligent assistants, activity tracking, and machine learning creates opportunities for personalized virtual and physical agents / robots that can positively impacts user health and quality of life. Well beyond providing information, such agents can serve as physical and mental health and education coaches and companions that support positive behavior change. However, sustaining user engagement and motivation over long-term interactions presents complex challenges. Our work over the past 15 years has addressed those challenges by developing human-machine (human-robot) interaction methods for socially assistive robotics that utilize multi-modal interaction data and expressive agent behavior to monitor, coach, and motivate users to engage in heath- and wellness-promoting activities. This talk will present methods and results of modeling, learning, and personalizing user motivation, engagement, and coaching of healthy children and adults, as well as stroke patients, Alzheimer's patients, and children with autism spectrum disorders, in short and long-term (month+) deployments in schools, therapy centers, and homes, and discuss research and commercial implications for technologies aimed at human daily use.