A Reinforcement-Learning-Based Approach for the Planning of Safety Strategies in AAL Applications

Cognitive Impairment diseases such as Alzheimer influence millions of people around the world. One usual trait of such diseases is that patients may perform irrational behaviors, which may result in danger to family members or to the patient him/herself. An Intelligent Ambient Assisted Living Systems should provide solutions able to react to those situations in order to guarantee the safety of the patient. Unfortunately, the unpredictable and irrational nature of patient's behaviors makes the development of such systems hard and complex. In this paper, we address this issue by presenting a reinforcement-learning-based solution to define a recovery process from dangerous situations by dynamically defining a safety strategy. The definition of an intelligent recovery process is the final goal of our work. We are testing the first prototype of our solution into a simulated environment