[TMP-045] Evidence-based chatbot interaction aimed at reducing sedentary behavior

Contact person: Michel Klein (michel.klein@vu.nl) 

Internal Partners:

1. Stichting VU ( Vrije Universiteit Amsterdam), Koen Hindriks, Michel Klein
2. University College London í(UCL), Yvonne Rogers

 

Interaction between chatbots and humans is often based on frequently occurring interaction patterns, e.g., question – answer. Those patterns usually describe a very brief phase in the interaction. In this micro project, we want to investigate whether we can design a chatbot for behavior change by including higher level patterns, which are adapted from the taxonomy of behavior change techniques (BCT’s). These patterns should describe the components of the interaction during a longer period of time. In addition, we investigate how to design a user interface in such a way that it sustains the interest of the users. We focus on reducing sedentary behavior, and especially sitting behavior, which can have negative health consequences. The interaction patterns and user interface will be implemented in a prototype. A user study evaluates the different components on effectiveness and engagement.

Results Summary

We show that the proposed approach provides high-quality semantic segmentation from the robot’s perspective, with accuracy comparable to the original one. In addition, we exploited the gained information and improved the recognition performance of the deep network for the lower viewpoints and showed that the small robot alone is capable of generating high-quality semantic maps for the human partner. The computations are close to real time, so the approach enables interactive applications.

Tangible Outcomes

1. Video presentation summarizing the project