Socially intelligent communication between users and smart systems is capable of reading, understanding, and utilizing user’s social signals such as emotion state, engagement, hesitation, cognitive load, and stress level. This brings the communication closer to the human-to-human communication and makes it simpler, more effective, and also pleasant for end user. As such is a core part of smart systems and without it the smart system cannot really support users.
In this project we built a socially intelligent communicator based on Python deployed to Docker system. It supports the connectivity of real time user sensing, experimenting, and building models of user’s social signals using machine learning.
The communicator was built by students only – several generations of students contributed to it and is designed to involve students interested in various related fields from sensors, machine learning, psychology of social signals and design.