HRI: Personalized Assistive Human-Robot Interaction: Validation in Socially-Assisted Post-Stroke Rehabilitation

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Robotics has the potential of positively impacting quality of life, especially for people with special needs. If we are to meet the demand for personalized one-on-one care for the growing populations of elderly individuals and those with special cognitive and social needs throughout life, great strides must be made in human-robot interaction (HRI) in order to bring robotics into everyday application domains. This interdisciplinary project identifies a specific set of HRI research questions in socially assistive robotics, the study of robotic systems capable of providing help through social rather than physical interaction. The research foci of the study are: embodiment, personality, empathy, and adaptivity toward the development of an assistive HRI model for customized time-extended assistive interaction. The research will be grounded in the stroke rehabilitation domain, where personalized and dedicated care is needed to provide supervision, motivation, and training during the critical post-stroke period and beyond, and where assistive HRI can play a key role. Specifically, a novel assistive HRI model will be developed based on personality matching between the user and the robot, in order to optimize the user's task performance on rehabilitation exercises. The model will be evaluated on multiple testbeds with a large pool of human subjects from the stroke patient population. An online learning algorithm will enable the robot to adapt to the user both over the course of short-term interactions during a single therapy sessions (e.g., in response to mood and fatigue), and time-extended interactions over multiple therapy sessions (e.g., in response to the evolving recovery process over months of rehabilitation). The work is the first to study the role of personality and empathy in assistive HRI with human subjects, as well as to engage in longitudinal assistive HRI research to assess time-extended human-machine interaction in the assistive context. An important contribution of the research is the unified and tightly integrated end-to-end approach, which combines key HRI issues of embodiment, personality, empathy and adaptivity in hypothesis-testing experiments. Project outcomes will also include a large and unique corpus of multi-modal data, which will be collected and analyzed, and made available to researchers across the relevant disciplines. The scientific impact will go well beyond novel insights toward a better understanding of the fundamentals of assistive HRI, and the role and potential for assistive human-machine interaction for stroke patient populations and rehabilitation in general.Broader Impacts: Currently there are about 750,000 new strokes per year in the United States, and some expect the number to double in the next twenty years with the growing elderly population. Project outcomes will provide pilot data necessary for translating the methodologies developed toward clinical applications.