Modeling Motivational States Through Interpreting Physical Activity Data for Adaptive Robot Companions

This research aims to develop an adaptive human-robot interaction system that works with users over long periods of time to achieve a common goal that is beneficial to the user. The particular scenario I focus on is that of a robot companion interacting with adolescents, helping them succeed at achieving daily physical activity goals. To develop such a system, I propose a method of modeling the user’s motivational state and employing this model in order to adapt motivational strategies best suited for each user. The proposed system uses both physical activity data obtained from wearable sensors (such as wristband devices) and information acquired by the robot from its interaction partners.

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