Situ-Centric Reinforcement Learning for Recommendation of Tasks in Activities of Daily Living In Smart Homes
Richard O. Oyeleke, Chen-Yeou Yu, Carl K. Chang · 2018
In this paper, we propose a new recommendation system that considers the changing human mental state, behavior and environmental contexts to provide guidance to persons diagnosed of mild cognitive impairment (MCI) or Alzheimer's in performing their activities of daily living (ADLs). The recommendation system is based on the Situ-framework that represents an activity as a sequence of situations, and a situation is a 3-tuplewhere M denotes human mental state, B is the behavior context and E is the environmental context. More so, it uses an automaton to model an activity thereby generating corresponding task sequences. Interactions between the inhabitants and the sensor networks and the activity being performed are represented as an information space. It then uses a modified model learning algorithm as its decision making tool to recommend appropriate actions or tasks sequence to the inhabitant in a situation of uncertainty caused by episode of confusion or memory loss to ensure he reaches his goal. Consequently, this helps ensure that safety of the person is not compromised when he makes a poor decision regarding the sequence in which ADL tasks are to be performed. We use a single activity class dataset generated from a smart home for 50 independent learning experiments for three (3) ADLs case studies. The accuracy of the sequential-tasks recommendations was found to be high. We further evaluated the risk sensitivity of the recommendation system using the action selection probabilities and corresponding reward points associated with performing a task. The results show that the proposed recommendation system is risk averse.