Modeling Reminder System for Dementia by Reinforcement Learning

Prospective memory refers to preparing, remembering and recalling plans that have been conceived in an intended manner. Various busyness and distractions can make people forget the activities that must be done the next time, especially for people with cognitive memory problems such as dementia. In this paper, we propose a reminder system with the idea of taking time and response into consideration to assist in remembering activities. Using the reinforcement learning method, this idea predicts the right time to remind users through notifications on smartphones. The notification delivery time will be adjusted to the user’s response history, which becomes feedback at any available time. Thus, users will get notifications based on the ideal time for each individual either, either with repetition or without repetition, so as not to miss the planned activity. By evaluating the dataset, the results show that our proposed modelling is able to optimize the time to send notifications. The eight alternative times to send notifications can be optimized to get the best time to notify the user with dementia. This implies that our algorithm propose can adjust to individual personality characteristics, which might be a stumbling block in dementia patient care, and solve multi-routine plan problems. Our propose can be useful for users with dementia because we can remind very well that the execution time of notifications is right on target, so it can prevent users with dementia from stressing out over a lot of notifications, but those who miss notifications can receive them back at a later time step, with the result that information on activities to be completed is still available.3rd International Conference on Activity and Behavior Computing, ABC 2021, 22 October 2021 through 23 October 2021, Onlinejournal articl