Interactive device control by using reinforcement learning in the ambient network environment and considering the adoption of human vital sign to get a user's condition

Abstract

The ambient networks need a function to select proper interactions in order to satisfy users. In this research, we propose a method to find the proper interactions to sleepy or sleeping users in a working space (e.g., lab and office) by reinforcement learning. This method controls a lamp, an aroma device and a music device and finds an interaction sequence that lets the users awake comfortably. We conducted an experiment to verify the validity of this method. In addition, we considered if measuring and analyzing a human vital sign (finger pulse) enable to get a user's condition.